Type:	Package
Title:	Blood Pressure Analysis in R
Version:	2.1.0
Maintainer:	John Schwenck <jschwenck12@gmail.com>
Description:	A comprehensive package to aid in the analysis of blood pressure data of all forms by providing both descriptive and visualization tools for researchers.
License:	GPL-3
URL:	https://github.com/johnschwenck/bp
BugReports:	https://github.com/johnschwenck/bp/issues
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.1.2
Imports:	dplyr, tidyr, magrittr, lubridate, ggplot2, gridExtra, cowplot, tibble, tidyselect, gtable
Depends:	R (≥ 3.5.0)
Suggests:	knitr, rmarkdown, png
VignetteBuilder:	knitr
Language:	en-US
NeedsCompilation:	no
Packaged:	2022-05-10 15:25:56 UTC; John
Author:	John Schwenck [aut, cre], Irina Gaynanova [aut]
Repository:	CRAN
Date/Publication:	2022-05-10 15:50:02 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Average Real Variability (ARV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_arv INSTEAD.

Usage

arv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = 0,
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Details

Calculate the Average Real Variability (ARV) at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE). ARV is a measure of dispersion that takes into account the temporal structure of the data and relies on the sum of absolute differences in successive observations, unlike the successive variation (SV) which relies on the sum of squared differences.

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• ARV_SBP / ARV_DBP: Calculates the average of the absolute differences between successive measurements. The resulting value averages across the granularity grouping for however many observations are present.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

References

Mena et al. (2005) A reliable index for the prognostic significance of blood pressure variability Journal of Hypertension 23(5).505-11, doi: 10.1097/01.hjh.0000160205.81652.5a.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

## Not run: 
# ARV Calculation
bp_arv(hypnos_proc, add_groups = c("SBP_Category"))
bp_arv(jhs_proc, inc_date = TRUE)

## End(Not run)

Average Real Variability (ARV)

Description

Calculate the Average Real Variability (ARV) at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE). ARV is a measure of dispersion that takes into account the temporal structure of the data and relies on the sum of absolute differences in successive observations, unlike the successive variation (SV) which relies on the sum of squared differences.

Usage

bp_arv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• ARV_SBP / ARV_DBP: Calculates the average of the absolute differences between successive measurements. The resulting value averages across the granularity grouping for however many observations are present.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

References

Mena et al. (2005) A reliable index for the prognostic significance of blood pressure variability Journal of Hypertension 23(5).505-11, doi: 10.1097/01.hjh.0000160205.81652.5a.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# ARV Calculation
bp_arv(hyp_proc, add_groups = c("SBP_Category"), bp_type = 'sbp')
bp_arv(jhs_proc, inc_date = TRUE)

Measures of Center (bp_center)

Description

Calculate the mean and median at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP, DBP, or both.

Usage

bp_center(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• SBP_mean / DBP_mean: Calculates the mean of systolic blood pressure readings for the specified time granularity.

• SBP_med / DBP_med: Calculates the median of systolic blood pressure readings for the specified time granularity.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# BP Center Calculation
bp_center(hyp_proc, subj = c(70417, 70435))

B-Proact1v Children Data

Description

The bp_children data set contains information on 1,283 children from Bristol, UK, who each took three blood pressure readings per visit over the course of two observation periods (at ages 9 and 11) and had their body mass index (BMI), physical activity, and sedentary time information recorded. The study examined how sedentary behavior and physical activity affected children progressing through primary school to better understand the relationship between elevated blood pressure in children and its impact on the development of cardiovascular disease into adulthood

Usage

bp_children

Format

A data frame containing 15 variables pertaining to blood pressure and physical activity as follows:

id

Child ID

reading

Index of the blood pressure reading

sbp

Systolic Blood Pressure (mmHg)

dbp

Diastolic Blood Pressure (mmHg)

gender

1 = Male
2 = Female

hh_educ

Household Highest Education:

1 = Up to GCSE/O level or equiv
2 = A level/NVQ or equiv
3 = Degree/HND or equiv
4 = Higher degree (MSc/PhD) or equiv

visit

Visit #

age

Age in Years

ht

Height (ft)

wt

Weight (lbs)

bmi

Body Mass Index

N.valid.days.all

Number of days of child accelerometer data overall

avg.mins.all

Average minutes per day over all valid days

sed.avg.mins.all

Average sedentary minutes per day over all valid days

mvpa.avg.mins.all

Average minutes of Moderate to Vigorous Physical Activity (MVPA) per day over all valid days

Details

(Solomon-Moore E, Salway R, Emm-Collison L, Thompson JL, Sebire SJ, Lawlor DA, Jago R (PI). 2020).

Licensed under a CC-BY Creative Commons Attribution 4.0 International

Source

Original Paper: doi: 10.1371/journal.pone.0232333

Data: doi: 10.5281/zenodo.1049587

Principal Investigator (PI): Russ Jago (University of Bristol)

Coefficient of Variation (CV)

Description

Calculate the coefficient of variation at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP, DBP, or both. CV is a measure of dispersion

Usage

bp_cv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• CV_SBP / CV_DBP: Calculates the ratio of standard deviation to the mean. CV_SBP or CV_DBP is useful for comparing the degree of variation from one data series to another.

• SD_SBP / SD_DBP: For completeness, the cv function also includes the standard deviation as a comparison metric to measure spread around the average.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# CV Calculation
bp_cv(hyp_proc, inc_date = TRUE, add_groups = "SBP_Category", bp_type = 'sbp')
bp_cv(jhs_proc, add_groups = c("meal_time"))
# Notice that meal_time is not a column from process_data, but it still works

Task Shifting and Blood Pressure Control in Ghana Data

Description

The bp_ghana data set includes 757 subjects across 32 community health centers who were partitioned into intervention groups according to a pragmatic cluster-randomized trial with 389 in the health insurance coverage (HIC) group and 368 in another group consisting of a combination of HIC with a nurse-led task-shifting strategy for hypertension control (TASSH) (this group is denoted TASSH + HIC). This study was an effort to assess the comparative effectiveness of HIC alone versus the combination of TASSH + HIC on reducing systolic blood pressure among patients with uncontrolled hypertension in Ghana. Baseline blood pressure measurements and 12 month follow-up results were collected among subjects, 85% of whom had 12 month data present

Usage

bp_ghana

Format

A data frame containing 21 variables pertaining to blood pressure as follows:

ID

Subject ID

Time_Elapsed

How much time elapsed between readings: Baseline measurement, 6 month follow-up, 12 month follow-up

SBP

Systolic Blood Pressure

DBP

Diastolic Blood Pressure

Age

Age in Years

Gender

0 = Male
1 = Female

EducationLevel

Highest Degree Earned:

1 = No Schooling
2 = Primary Schooling (Grades 1 to 6)
3 = Junior Secondary Schooling (JSS) (Grades 7-8)
4 = Secondary School (Grades 9-11)
5 = Completed Secondary School
6 = Technical school certificate
7 = Some college but no degree
8 = Associate degree
9 = Graduate or Professional school (MD, JD, etc.)
999 = Not specified

EmploymentStatus

0 = Unemployed
1 = Employed

Literacy

0 = Illiterate
1 = Literate

Smoking

1 = Smoker
2 = Occasional
3 = Ex-Smoker
4 = Non-Smoker

Income

Income level expressed in home currency - Ghanaian cedi (GhC)

SiteNumber

Site Location in Ghana:

1 = Suntreso
2 = Ananekrom
3 = Manhiya
4 = Apatrapa
5 = Nkawie
6 = Afrancho
7 = Kumsai South
8 = Mampongteng
9 = Mampong
10 = Bomfa
11 = Ejura
12 = Kofiase
13 = Kokofu
14 = Asuofia
15 = Konongo
16 = Subirisu
17 = Effiduase
18 = Abuakwa
19 = Tafo
20 = KMA
21 = Methodist
22 = Kenyasi
23 = Asonamaso
24 = Juansa
25 = Juaso
26 = Sekyredumase
27 = Nkenkaasu
28 = Berekese
29 = Bekwai
30 = Dwease
31 = Kuntanese
32 = Foase

CVRisk

Cardiovascular Risk Assessment (%) at Baseline:

1) <10%
2) 10-20%
3) 20-30%
4) 30 to 40%
999= Missing Data

BMI

Body Mass Index

BMIClassification

Classification of BMI:

1 = Underweight (<18.5 kg/m^2)
2 = Normal (18.5 to 24.9 kg/m^2)
3 = Overweight (25.0 to 29.9 kg/m^2)
4 = Obese (>30 kg/m^2)

PhysicalActivity

All activity in weighted MET minutes (per week) at Baseline

TrtorCtrl

0 = Control
1 = Treatment

RuralUrban

0 = Rural
1 = Urban

DoctorsAvailable

# of doctors on staff

NursesAvailable

# of nurses on staff

Patientsannually

# of patients seen annually

Details

(Ogedegbe G, Plange-Rhule J, Gyamfi J, Chaplin W, Ntim M, Apusiga K, Iwelunmor J, Awudzi KY, Quakyi KN, Mogavero JN, Khurshid K, Tayo B, Cooper R. 2019).

Licensed under a CC-0 1.0 (Creative Commons) Universal Public Domain Dedication License

Source

Original Paper: doi: 10.1371/journal.pmed.1002561

Data: doi: 10.5061/dryad.16c9m51

Blood Pressure Histograms

Description

The bp_hist function serves to display the frequencies of the SBP and DBP readings. These histograms are formatted to complement the bp_scatter function.

Usage

bp_hist(data, subj = NULL, bins = 30, na.rm = TRUE)

Arguments

Value

A list containing four objects: three histogram visual graphics corresponding to the SBP / DBP totals, SBP frequency, and DBP frequency, and a fourth list element corresponding to a plot legend object for use in the bp_report function

Examples

data("bp_jhs")
data("bp_hypnos")
hyp_proc <- process_data(bp_hypnos,
                         bp_type = 'abpm',
                         sbp = "syst",
                         dbp = "DIAST",
                         date_time = "date.time",
                         id = "id",
                         wake = "wake",
                         visit = "visit",
                         hr = "hr",
                         map = "map",
                         rpp = "rpp",
                         pp = "pp",
                         ToD_int = c(5, 13, 18, 23))

jhs_proc <- process_data(bp_jhs,
                         sbp = "Sys.mmHg.",
                         dbp = "Dias.mmHg.",
                         date_time = "DateTime",
                         hr = "pulse.bpm.")
rm(bp_hypnos, bp_jhs)

bp_hist(hyp_proc)
bp_hist(jhs_proc)

HYPNOS Study - 5 Subject Sample

Description

ABPM measurements for 5 subjects with Type II diabetes. These data are part of a larger study sample that consisted of patients with Type 2 diabetes recruited from the general community. To be eligible, patients with Type 2 diabetes, not using insulin therapy and with a glycosylated hemoglobin (HbA_1c) value at least 6.5

Usage

bp_hypnos

Format

A data frame with the following columns:

NR.

Integer. The index corresponding to the reading of a particular subject for a given visit.

DATE.TIME

Character. The date-time value corresponding to the given reading.

SYST

Integer. The systolic blood pressure reading.

MAP

Integer. The mean arterial pressure value.

DIAST

Integer. The diastolic blood pressure reading.

HR

Integer. The heart rate value (measured in beats per minute – bpm).

PP

Integer. The pulse pressure value calculated as the systolic value - the diastolic value.

RPP

Integer. The rate pressure product calculated as the systolic reading multiplied by the heart rate value.

WAKE

Integer. A logical indicator value corresponding to whether or not a subject is awake (WAKE = 1) or not.

ID

Integer. A unique identifier for each subject.

VISIT

Integer. A value associated with the visit number or a particular subject.

DATE

Character. A date-valued column indicating the date of the given reading. Dates are specified according to their actual date (i.e. 01:00 corresponds to the next date, even if the subject is awake. Awake-state is indicated via WAKE).

Blood Pressure - 1 Subject - John Schwenck

Description

Single-subject self-monitored blood pressure readings over 108 days (April 15, 2019 - August 01, 2019). This data set has been processed and uploaded to the Harvard Dataverse for public use. It contains variables pertaining to Date/Time, Systolic BP, Diastolic BP, and Heart Rate. The data assumes a threshold blood pressure of 135 / 85 which is used to calculate excess amounts.

Usage

bp_jhs

Format

A data frame with the following columns:

DateTime

A POSIXct-formatted column corresponding to the date and time of the corresponding reading in local time.

Month

Integer. The month corresponding to the Date column.

Day

Integer. The day of the month corresponding to the Date column.

Year

Integer. The year corresponding to the Date column.

DayofWk

Character. The day of the week corresponding to the Date column.

Hour

Integer. The hour corresponding to the DateTime column.

Meal_Time

Character. The estimated meal time corresponding to the DateTime column.

Sys.mmHg.

Integer. The systolic blood pressure reading.

Dias.mmHg.

Integer. The diastolic blood pressure reading.

bpDelta

Integer. The difference between the Sys.mmHg. and Dias.mmHg. column. This is also known as the Pulse Pressure.

Pulse.bpm.

Integer. The heart rate value (measured in beats per minute – bpm).

Source

doi: 10.7910/DVN/EA1SAP

Blood Pressure Magnitude (Peak and Trough)

Description

Calculate the Peak and Trough defined as the max BP - average BP and average BP - min BP, respectively.

Usage

bp_mag(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• Peak_SBP / Peak_DBP: Measures the difference between the max value and the average

• Trough_SBP / Trough_DBP: Measures the difference between the average and the min value

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# BP Magnitude Calculation
bp_mag(hyp_proc)
bp_mag(jhs_proc, inc_date = TRUE)

Pregnancy Day Assessment Clinic Data

Description

The bp_preg data set includes 209 women each with 30 minute recordings during the Pregnancy Day Assessment Clinic (PDAC) observation window for up to a maximum of 240 minutes (i.e. a maximum of 8 total readings per subject per observation window in addition to an initial "booking" reading before the PDAC assessment). This recent study in obstetrics and gynecology investigated pregnancy-induced hypertension (PIH) and pre-eclampsia (PE) prediction by analyzing cardiac and physiological information to determine whether the blood pressure assessment of the first observation window of 1 hour (60 minutes) was sufficient relative to the standard 4 hour (240 minute) window.

Usage

bp_preg

Format

A data frame containing 55 variables related to physiological assessment during pregnancy. The data variable dictionary is as follows:

Abdominal.pain

Binary indicator for whether an individual exhibits abdominal pain (1 = yes) or not (0 = no)

Accelerations

Fetal heart rate accelerations

Admission

Admission from PDAC

Age

Maternal age at estimated date of confinement

ALP

Alkaline phosphatase u/L

ALT

Alanaine transaminase u/L

AN_PET

Binary indicator for Antenatal diagnosis of pre-eclampsia 1 = yes, 0 = no

Antihypertensive.Meds

Current antihypertensive medication

Asymptomatic

Binary indicator for whether an individual is asymptomatic (1 = yes) or not (0 = no)

Baseline

Baseline fetal heart rate (normal, bradycardia, tachycardia)

BMI

Body Mass Index

Creatinine

micromol/L

CTG

Cardiotocogram

D_att_Dryad

Date of attendance modified by adding a random number of days between -7 and +7

DBP

Diastolic Blood Pressure

Decelerations

Fetal heart rate decelerations

DOB_Dryad

Maternal date of birth modified by adding a random number of days between -7 and +7

DrRV

Medical review during PDAC

EDC_Dryad

Estimated date of confinement modified by adding a random number of days between -7 and +7

EmCS

Emergency Caesarean section

Final.Diagnosis.

Final diagnosis: 0 = nil, 1 = pregnancy induced hypertension, 2 = pre-eclampsia

GA_att

Gestational age (weeks) at attendance

GGT

Gamma glutamyl transaminase u/L

Grav

Gravidity: number of pregnancies including the current one

Headache

Binary indicator for whether an individual has a headache (1 = yes) or not (0 = no)

Ht

Maternal Height (cm)

Hyperreflexia

Binary indicator for whether an individual exhibits Hyperreflexia (1 = yes) or not (0 = no)

IOL

Induction of labour

ID

Subject ID #

IP_PET

Intrapartum diagnosis of pre-eclampsia 1 = yes, 0 = no

Nausea

Binary indicator for whether an individual exhibits Nausea (1 = yes) or not (0 = no)

Oedema

Binary indicator for whether an individual exhibits Oedema (1 = yes) or not (0 = no)

Para

Parity: number of pregnancies proceding to 20 or more weeks, not including the current one

PHx_ASA

Prescribed aspirin in this pregnancy

PHx_Eclampsia

Past history of eclampsia

PHx_EssBP

Past history of essential hypertension

PHx_PET

Past history of pre-eclampsia

PHx_Smoker

Smoking history

PIERS

Pre-eclampsia Integrated Estimate of Risk (%)

PN_PET

Postnatal diagnosis of pre-eclampsia 1 = yes, 0 = no

Platelets

10^9 per mL

PrevPDAC

Number of previous PDAC assessments this pregnancy

Privacy

Generic privacy consent form permits participation in audit

SBP

Systolic Blood Pressure

SOB

Binary indicator for whether an individual exhibits Shortness of Breath (SOB) (1 = yes) or not (0 = no)

Safe.for.discharge.at.1.hour.

Binary indicator for whether an individual is safe for discharge at 1 hour (1 = yes) or not (0 = no)

Safe.for.discharge.at.1.hour....Other..please.specify.

Binary indicator for whether an individual is safe for discharge at 1 hour - other - comments

Self.discharge

Self-discharge from PDAC

Time_Elapsed

30 minute recordings during the Pregnancy Day Assessment Clinic (PDAC) observation window for up to a maximum of 240 minutes including the "Booking" recording

Urate

mmol/L

Urea

micromol/L

UrinePCR

Spot urine protein:creatinine ratio mg/mmol

Variability

Fetal heart rate variability as judged visually

Visual.Disturbances

Binary indicator for whether an individual exhibits Visual Disturbances (1 = yes) or not (0 = no)

Wt

Pre-pregnancy or early pregnancy weight (kg)

Details

(McCarthy EA, Carins TA, Hannigan Y, Bardien N, Shub A, Walker S. 2015)

Licensed under a CC-0 1.0 (Creative Commons) Universal Public Domain Dedication License

Source

doi: 10.5061/dryad.0bq15

Blood Pressure Range

Description

Calculates the range (max - min) of both SBP and DBP values in addition to max and min values for reference with the option to specify date as an additional level of granularity

Usage

bp_range(
  data,
  inc_date = FALSE,
  subj = NULL,
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble with SBP_max, SBP_min, SBP_range, DBP_max, DBP_min, DBP_range and any additional optional columns included in data such as ID, VISIT, WAKE, and DATE. If inc_date = TRUE, each row will correspond to a date. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• SBP_max / DBP_max: Finds the maximum value for the given grouping granularity

• SBP_min / DBP_min: Finds the minimum value for the given grouping granularity

• SBP_range / DBP_range: Calculates the range between the max and min values

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load bp_hypnos
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# Calculate BP range
bp_range(hypnos_proc)
bp_range(jhs_proc, inc_date = TRUE, add_groups = c("meal_time"))
# Notice that meal_time is not a column from process_data, but it still works

Blood Pressure in Salt-Sensitive Dahl Rats Data

Description

The bp_rats data set includes arterial blood pressure waveform time series data of the SS(n = 9) and SS.13 (n = 6) genetic strains of Dahl rats sampled at 100 Hz who were each administered a low and high salt diet. It is taken from the study of Bugenhagen et al.(2010) which sought to investigate the origins of the baroreflex dysfunction in salt-sensitive Dahl rats on Hypertension.

Usage

bp_rats

Format

A data frame containing 5 variables pertaining to arterial continuous waveform blood pressure as follows:

rat_type

The type of a particular Dahl Rat (either SS or SSBN13) corresponding to the sodium intake administered:

ss_hs = SS rat given the high sodium diet
ss_ls = SS rat given the low sodium diet
ssbn13_hs = SSBN13 rat given the high sodium diet
ssbn13_ls = SSBN13 rat given the low sodium diet

rat_ID

ID # corresponding to a particular rat. There are 9 SS rats and 6 SSBN13 rats.

ABP

Continuous waveform data of each rat's arterial (blood) pressure sampled at 100 Hz.

time_sec

Amount of time elapsed, expressed in seconds

time_min

Amount of time elapsed, expressed in minutes

Details

(Goldberger A., Amaral L., Glass L., Hausdorff J., Ivanov P. C., Mark R., Bugenhagen S.M., Cowley A.W. Jr, Beard D.A., ... \& Stanley H. E. 2000).

Licensed under a ODC-BY (Creative Commons) Open Data Commons Attribution License 1.0

Source

Original Paper: doi: 10.1152/physiolgenomics.00027.2010

Data: doi: 10.13026/C20597

Blood Pressure Report

Description

The bp_report function serves to aggregate various data visuals and metrics pertaining to the supplied data set into a clean formatted report.

Usage

bp_report(
  data,
  subj = NULL,
  inc_low = TRUE,
  inc_crisis = TRUE,
  group_var = NULL,
  save_report = TRUE,
  path = NULL,
  filename = "bp_report",
  width = 11,
  height = 8.5,
  filetype = "pdf",
  units = "in",
  scale = 1.25,
  plot = TRUE,
  hist_bins = 30
)

Arguments

Value

If plot = TRUE, the function produces a plot of BP report that contains scatterplot of BP values by stages (see bp_scatter), histograms of BP values by stages (see bp_hist) and frequency tables of BP values by stages and day of the week/time of the day (see dow_tod_plots). If plot = FALSE, the function returns the grob object that can be plotted later using grid.arrange. If save_report = TRUE, the report will be automatically saved at the current working directory (can be checked using getwd()) or at specified file path.

Examples

data("bp_jhs")
data("bp_hypnos")
hyp_proc <- process_data(bp_hypnos,
                         sbp = "syst",
                         dbp = "DIAST",
                         date_time = "date.time",
                         id = "id",
                         wake = "wake",
                         visit = "visit",
                         hr = "hr",
                         map = "map",
                         rpp = "rpp",
                         pp = "pp",
                         ToD_int = c(5, 13, 18, 23))

jhs_proc <- process_data(bp_jhs,
                         sbp = "Sys.mmHg.",
                         dbp = "Dias.mmHg.",
                         date_time = "DateTime",
                         hr = "pulse.bpm.")
rm(bp_hypnos, bp_jhs)

## Not run: 
# Single-subject Report
# save_report = FALSE for illustrative purposes
# plot = TRUE will automatically generate a plot
bp_report(jhs_proc, save_report = FALSE, plot = TRUE)

# Multi-subject Report
# save_report = FALSE for illustrative purposes
# plot = FALSE will suppress the plot output and return a grob object
out = bp_report(hyp_proc, group_var = 'VISIT', save_report = FALSE, plot = FALSE)
gridExtra::grid.arrange(out)

## End(Not run)

Blood Pressure Stage Scatter Plot

Description

Display all SBP and DBP readings on a scatterplot with deliniation of BP according to the 8 mutually exclusive levels of Hypertension as in Lee et al (2020) (the default), or the levels set by the American Heart Association (AHA).

Usage

bp_scatter(
  data,
  plot_type = c("stages2020", "AHA"),
  subj = NULL,
  group_var = NULL,
  wrap_var = NULL,
  inc_crisis = TRUE,
  inc_low = TRUE
)

Arguments

Details

There are eight total stages according to Lee et al (2020) with the options to include two additional categories for "Low" (Hypotension) and Hypertensive "Crisis". The categories are as follows:

• Low - (Optional) Legacy category for consistency with AHA stages. According to the AHA, low blood pressure is any reading with SBP < 100 and DBP < 60, and is depicted in light blue in the scatter plot. This is always displayed in "AHA" plot, and can be displayed in "stages2020" plot by setting inc_low = TRUE.

• Normal - SBP readings less than 120 and DBP readings less than 80. Reading within this range that either have SBP > 100 or DBP > 60 are also considered Normal by AHA. Normal BP is depicted in green in the scatter plot.

• Elevated - SBP readings between 120 - 129 and DBP readings less than 80. Coincides with Elevated stage as defined by AHA. Without intervention to control the condition, individuals are likely to develop Hypertension. Elevated BP is depicted in yellow in the scatter plot.

• Stage 1 - All (SDH) - SBP readings between 130 - 139 and DBP readings between 80 - 89. Stage 1 Hypertension will typically result in doctors prescribing medication or lifestyle changes. Stage 1 BP is depicted in dark orange in the scatter plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 1 - Isolated Diastolic Hypertension (IDH) - SBP readings less than 130, but DBP readings between 80 - 89. This alternative stage 1 level accounts for unusually high diastolic readings, but fairly normal systolic readings and is depicted in orange in the plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 1 - Isolated Systolic Hypertension (ISH) - SBP readings between 130 - 139, but DBP readings less than 80. This alternative stage 1 level accounts for unusually high systolic readings, but fairly normal diastolic readings and is depicted in orange in the plot. These readings correspond to Stage 1 as defined by AHA.

• Stage 2 - All (SDH) - SBP readings between 140 - 180 and DBP readings between 90 - 120. Stage 2 Hypertension will typically result in doctors prescribing both medication and lifestyle changes. Stage 2 BP is depicted in bright red in the scatter plot. These readings correspond to Stage 2 as defined by AHA.

• Stage 2 - Isolated Diastolic Hypertension (IDH) - SBP readings less than or equal to 140, but DBP readings greater than or equal to 90. This alternative stage 2 level accounts for unusually high diastolic readings, but fairly normal systolic readings and is depicted in red. These readings correspond to Stage 2 as defined by AHA.

• Stage 2 - Isolated Systolic Hypertension (IDH) - SBP readings greater than or equal to 140, but DBP readings less or equal to 90. This alternative stage 2 level accounts for unusually high systolic readings, but fairly normal diastolic readings and is depicted in red. These readings correspond to Stage 2 as defined by AHA.

• Crisis - (Optional) Legacy category for consistency with AHA stages. According to the AHA, hypertensive crisis is defined as a SBP reading exceeding 180 or a DBP reading exceeding 120. This stage requires medical attention immediately. Crisis is depicted in red in the scatter plot. This is always displayed in "AHA" plot, and can be displayed in "stages2020" plot by setting inc_crisis = TRUE.

Value

A scatter plot graphic using the ggplot2 package overlaying each reading (represented as points) onto a background that contains each stage

References

Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, Kim HC. Cardiovascular risk of isolated systolic or diastolic hypertension in young adults. Circulation. 2020; 141:1778–1786. doi: 10.1161/CIRCULATIONAHA.119.044838

Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., ... & Schutte, A. E. (2020). 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension, 75(6), 1334-1357. doi: 10.1161/HYPERTENSIONAHA.120.15026

Examples

data("bp_jhs")
data("bp_hypnos")
data("bp_ghana")
hypnos_proc <- process_data(bp_hypnos,
                         bp_type = 'abpm',
                         sbp = "syst",
                         dbp = "DIAST",
                         date_time = "date.time",
                         id = "id",
                         wake = "wake",
                         visit = "visit",
                         hr = "hr",
                         map = "map",
                         rpp = "rpp",
                         pp = "pp",
                         ToD_int = c(5, 13, 18, 23))

jhs_proc <- process_data(bp_jhs,
                         sbp = "Sys.mmHg.",
                         dbp = "Dias.mmHg.",
                         date_time = "DateTime",
                         hr = "pulse.bpm.")
rm(bp_hypnos, bp_jhs)

# HYPNOS Data
bp_scatter(hypnos_proc,
           inc_crisis = TRUE,
           inc_low = TRUE,
           group_var = "wake",
           wrap_var = "day_of_week")

# JHS Data
bp_scatter(jhs_proc,
           plot_type = "AHA",
           group_var = "time_of_day")

# Ghana Data Set
#(Note that column names are of proper naming convention so no processing needed)
bp_scatter(bp::bp_ghana, inc_crisis = TRUE, inc_low = FALSE, group_var = "TIME_ELAPSED")

Nocturnal Blood Pressure Metrics

Description

The bp_sleep_metrics function serves to calculate nocturnal metrics from relevant medical literature.

Usage

bp_sleep_metrics(data, subj = NULL)

Arguments

Details

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period; "sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective, indication of sleep is currently obtained through means of EEG or other highly specialized equipment. For all intents and purposes, sleep in the context of this package refers to actigraphy-inferred nocturnal periods of rest.

The calculation of BP metrics related to sleep is based on averages of BP readings from four periods as identified in Kairo et al. (2003): presleep or evening (2 hours before sleep start), prewake (2 hours before wake), postwake or morning (2 hours after wake), lowest (3 measurements centered at the minimal BP reading over sleep). The function uses WAKE column to automatically allocate BP measurements to various periods. The following metrics are defined as a function of the period averages (separately for SBP and DBP)

dip_calc = 1 - mean_sleep_BP/mean_wake_BP (dip proportion)

noct_fall = mean_presleep_BP - mean_lowest_BP (nocturnal fall)

ST_mbps = mean_postwake_BP - mean_lowest_BP (sleep through morning blood pressure surge)

PW_mbps = mean_postwake_BP - mean_prewake_BP (prewake morning blood pressure surge)

ME_avg = (mean_presleep_BP + mean_postwake_BP)/2 (morning-evening average)

ME_diff = mean_postwake_BP - mean_presleep_BP (morning-evening difference)

wSD = ( (wake_SD x HRS_wake) + (sleep_SD x HRS_sleep) ) / (HRS_wake + HRS_sleep) (weighted standard deviation)

Value

The function outputs a list containing 4 tibble objects corresponding to the following tables:

References

Kario, K., Pickering, T. G., Umeda, Y., Hoshide, S., Hoshide, Y., Morinari, M., ... & Shimada, K. (2003). Morning surge in blood pressure as a predictor of silent and clinical cerebrovascular disease in elderly hypertensives: a prospective study. Circulation, 107(10), 1401-1406.

Examples

hypnos_proc <- process_data(bp_hypnos,
                           sbp = "syst",
                           dbp = "DIAST",
                           date_time = "date.time",
                           id = "id",
                           wake = "wake",
                           visit = "visit",
                           hr = "hr",
                           map = "map",
                           rpp = "rpp",
                           pp = "pp",
                           bp_type = "abpm")

bp_sleep_metrics(hypnos_proc)

Alternative Blood Pressure Stages

Description

Adds BP_CLASS, SBP_Category, and DBP_Category columns to supplied dataframe.

Usage

bp_stages(
  data,
  sbp,
  dbp,
  inc_low = TRUE,
  inc_crisis = TRUE,
  data_screen = TRUE,
  SUL = 240,
  SLL = 50,
  DUL = 140,
  DLL = 40,
  adj_sbp_dbp = TRUE
)

Arguments

Details

Supplied dataframe must adhere to the unified format using the process_data function.

Value

A dataframe with additional columns corresponding to the stages of high blood pressure and the supplementary SBP / DBP categories

References

Omboni, S., Parati, G*., Zanchetti, A., Mancia, G. Calculation of trough: peak ratio of antihypertensive treatment from ambulatory blood pressure: methodological aspects Journal of Hypertension. October 1995 - Volume 13 - Issue 10 - p 1105-1112 doi: 10.1097/00004872-199510000-00005

Examples

# Load bp_hypnos
data(bp_hypnos)

bp_stages(bp_hypnos, sbp = "syst", dbp = "diast")


# Load bp_jhs data
data(bp_jhs)

bp_stages(bp_jhs, sbp = "sys.mmhg.", dbp = "dias.mmhg.")

Aggregated BP Summary Statistics

Description

Combines the output from the following functions:

• bp_center

• bp_cv

• bp_arv

• bp_sv

• bp_mag

• bp_range

Usage

bp_stats(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date, if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• Output from the following functions: bp_center, bp_cv, bp_arv, bp_sv, bp_mag, bp_range

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hyp_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# BP Stats Aggregated Table
bp_stats(hyp_proc, subj = c(70417, 70435), add_groups = c("SBP_Category"), bp_type = 'sbp')
bp_stats(jhs_proc, add_groups = c("SBP_Category"))

Successive Variation (SV)

Description

Calculate the successive variation (SV) at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE)for either SBP, DBP, or both. SV is a measure of dispersion that takes into account the temporal structure of the data and relies on the sum of squared differences in successive observations, unlike the average real variability (ARV) which relies on the sum of absolute differences. $$SV = sqrt(sum(x_i+1 - x_i)^2/n-1)$$

Usage

bp_sv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = c("both", "sbp", "dbp"),
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Details

NOTE: The canonical standard deviation, independent of the temporal structure using the sample average, is added for comparison: $$SD = sqrt(sum(x_i+1 - xbar)^2/n-1)$$

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• SV_SBP / SV_DBP: Calculates the square root of the average squared differences between successive measurements. The resulting value averages across the granularity grouping for however many observations are present.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# SV Calculation
bp_sv(hypnos_proc)
bp_sv(jhs_proc, add_groups = c("meal_time"))
# Notice that meal_time is not a column from process_data, but it still works

Blood Pressure Tables

Description

Generate a list of pertinent table outputs that detail various information specific to blood pressure.

Usage

bp_tables(
  data,
  bp_type = c("both", "sbp", "dbp"),
  bp_perc_margin = NULL,
  wake_perc_margin = 2,
  subj = NULL
)

Arguments

Value

A list of table outputs for various subsets of the data based on which bp_type is selected

Examples

data("bp_jhs")
data("bp_hypnos")
hyp_proc <- process_data(bp_hypnos,
                         bp_type = 'abpm',
                         sbp = "syst",
                         dbp = "DIAST",
                         date_time = "date.time",
                         id = "id",
                         wake = "wake",
                         visit = "visit",
                         hr = "hr",
                         map = "map",
                         rpp = "rpp",
                         pp = "pp",
                         ToD_int = c(5, 13, 18, 23))

jhs_proc <- process_data(bp_jhs,
                         sbp = "Sys.mmHg.",
                         dbp = "Dias.mmHg.",
                         date_time = "DateTime",
                         hr = "pulse.bpm.")
rm(bp_hypnos, bp_jhs)

bp_tables(jhs_proc)
bp_tables(hyp_proc)

Blood Pressure Time Series Plots

Description

Blood Pressure Time Series Plots

Usage

bp_ts_plots(
  data,
  index = NULL,
  subj = NULL,
  first_hour = 0,
  rotate_xlab = FALSE,
  wrap_var = NULL,
  wrap_row = NULL,
  wrap_col = NULL,
  method = NULL,
  formula = NULL
)

Arguments

Value

If the data does not contain a DATE_TIME column, a single list will be returned with the time-dependent plots for each subject ID. If the data does contain a DATE_TIME column (and index is not specified), a list of two lists will be returned for each subject ID: one corresponding to the time-dependent plots (according to the DATE_TIME values), and another plot corresponding to the HOUR plots which show repeated measurements of BP values throughout a 24-hour period. The index of the output therefore corresponds to whether there is only the time-dependent plot type (the former situation) or there are both time-dependent and hourly plot types (the latter situation).

Examples

# Pregnancy Data Set

# bp_preg requires the use of the index argument since there are no DATE or
# DATE_TIME columns available
data_preg <- bp::bp_preg
data_preg$Time_Elapsed <- factor(data_preg$Time_Elapsed,
     levels = c("Booking", "0", "30", "60", "90", "120", "150", "180", "210", "240"))

bp::bp_ts_plots(data_preg, index = 'time_elapsed', subj = 1:3)


# JHS Data Set

# bp_jhs returns two lists since there is a DATE_TIME column: one for
# DATE_TIME and one for HOUR
data_jhs <- bp::process_data(bp::bp_jhs,
                                    sbp = 'sys.mmhg.',
                                    dbp = 'dias.mmhg.',
                                     hr = 'pulse.bpm.',
                              date_time = 'datetime')

bp::bp_ts_plots(data_jhs)


# HYPNOS Data Set

# bp_hypnos wraps the plots by the visit # since each subject was recorded over
# the course of two office visits
data_hypnos <- bp::process_data(bp::bp_hypnos,
                                          sbp = 'syst',
                                          dbp = 'diast',
                                    date_time = 'date.time')

bp::bp_ts_plots(data_hypnos, wrap_var = 'visit', subj = '70435')

Create Groups for Dplyr

Description

Used in the following functions: arv, bp_mag, bp_range, cv, sv, bp_center, bp_stats

Usage

create_grps(data, inc_date, add_groups, inc_wake)

Arguments

Value

A vector of string values corresponding to the column names that will subset / group the data in dplyr functions

Coefficient of Variation (CV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_cv INSTEAD.

Usage

cv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = 0,
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Details

Calculate the coefficient of variation at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE) for either SBP, DBP, or both. CV is a measure of dispersion

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• CV_SBP / CV_DBP: Calculates the ratio of standard deviation to the mean. CV_SBP or CV_DBP is useful for comparing the degree of variation from one data series to another.

• SD_SBP / SD_DBP: For completeness, the cv function also includes the standard deviation as a comparison metric to measure spread around the average.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

## Not run: 
# CV Calculation
bp_cv(hypnos_proc, inc_date = TRUE, add_groups = "SBP_Category")
bp_cv(jhs_proc, add_groups = c("meal_time"))
# Notice that meal_time is not a column from process_data, but it still works

## End(Not run)

Nocturnal Blood Pressure Dipping Calculation

Description

Calculate the percent and average decline (or potentially reverse) in nocturnal blood pressure. This function is typically used with ABPM data, or at minimum, data with a corresponding a WAKE column available to indicate awake vs asleep.

Nocturnal blood pressure decline is an indicator of an individual's natural circadian rhythm. Studies have shown that individuals with diminished circadian rhythms are more likely to exhibit target organ damage. There is a "U-shaped" relationship that exists among the magnitude of nocturnal blood pressure decline; the extreme dippers and the non-dippers (including reverse dippers) are both more prone to mortality risk than normal dippers.

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period; "sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective, indication of sleep is currently obtained through means of EEG or other highly specialized equipment. For all intents and purposes, sleep in the context of this package refers to actigraphy-inferred nocturnal periods of rest.

Usage

dip_calc(
  data,
  sleep_start_end = NULL,
  dip_thresh = 0.1,
  extreme_thresh = 0.2,
  inc_date = FALSE,
  subj = NULL
)

Arguments

Value

A list containing 2 tibble objects. The first tibble object lists grouped average values for SBP and DBP for awake and asleep periods. The second dip_pct tibble object lists the dipping percentage and classification according to the results from the first dip tibble. If inc_date = TRUE these two tibbles will be broken down further by date. There are 4 classifications a subject can have (assuming a default dipping threshold of 10% and extreme dipping threshold of 20% according to the original source):

• Reverse Dipper - no nocturnal decline (greater or equal to 0%)

• Non-Dipper - a nocturnal decline between 0 - 10%

• Dipper - a nocturnal decline between 10% and the extreme dipping % (20%)

• Extreme Dipper - a nocturnal decline exceeding 20%

References

Okhubo, T., Imai, Y., Tsuji, K., Nagai, K., Watanabe, N., Minami, J., Kato, J., Kikuchi, N., Nishiyama, A., Aihara, A., Sekino, M., Satoh, H., and Hisamichi, S. (1997). Relation Between Nocturnal Decline in Blood Pressure and Mortality: The Ohasama Study, American Journal of Hypertension 10(11), 1201–1207, doi: 10.1016/S0895-7061(97)00274-4.

Examples

## Load bp_hypnos
data(bp_hypnos)

## Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos,
                     sbp = 'syst',
                     dbp = 'diast',
                     date_time = 'date.time',
                     hr = 'hr',
                     pp = 'PP',
                     map = 'MaP',
                     rpp = 'Rpp',
                     id = 'id',
                     visit = 'Visit',
                     wake = 'wake')

dip_calc(hypnos_proc)

Plot of Dipping Classifications

Description

Graphical visualization of the dip_calc output for categories of dipping percentages.

NOTE: Any reference to "sleep" in the bp package refers to an individual's nocturnal period; "sleep" is used in an informal sense for intuitive purposes. Technically, from a clinical perspective, indication of sleep is currently obtained through means of EEG or other highly specialized equipment. For all intents and purposes, sleep in the context of this package refers to actigraphy-inferred nocturnal periods of rest.

Usage

dip_class_plot(
  data,
  subj = NULL,
  dip_thresh = 0.1,
  extreme_thresh = 0.2,
  thresh_mult = 2,
  sleep_start_end = NULL
)

Arguments

Value

A scatter plot of all dipping percentage values layered on top of the category plot outlined in Okhubo et al. (1995). dip_thresh and extreme_thresh denote the cutoffs for the Normal and Extreme dipping categories. Any dips below zero are denoted as Inverted (or Reverse) dipping.

The default plot categories are as follows:

• INV: Inverted (Reverse) Dipper - no nocturnal decline (greater or equal to 0%)

• ND: Non-Dipper - a nocturnal decline between 0 - 10%

• DIP: Dipper - a nocturnal decline between 10% and the extreme dipping % (20%)

• ED: Extreme Dipper - a nocturnal decline exceeding 20%

References

Okhubo, T., Imai, Y., Tsuji, K., Nagai, K., Watanabe, N., Minami, J., Kato, J., Kikuchi, N., Nishiyama, A., Aihara, A., Sekino, M., Satoh, H., and Hisamichi, S. (1997). Relation Between Nocturnal Decline in Blood Pressure and Mortality: The Ohasama Study, American Journal of Hypertension 10(11), 1201–1207, doi: 10.1016/S0895-7061(97)00274-4.

Examples

hypnos_proc <- process_data(bp::bp_hypnos,
                               sbp = "syst",
                               dbp = "DIAST",
                               date_time = "date.time",
                               id = "id",
                               wake = "wake",
                               visit = "visit")

dip_class_plot(hypnos_proc)

Blood Pressure Tables in Graphical Format

Description

Blood Pressure Tables in Graphical Format

Usage

dow_tod_plots(data, subj = NULL)

Arguments

Value

A list of four 'gtables' that correspond to the tables for Day of Week and Time of Day broken down by both SBP and DBP.

Examples

data("bp_hypnos")
hyp_proc <- process_data(bp_hypnos,
                         bp_type = 'abpm',
                         sbp = "syst",
                         dbp = "DIAST",
                         date_time = "date.time",
                         id = "id",
                         wake = "wake",
                         visit = "visit",
                         hr = "hr",
                         map = "map",
                         rpp = "rpp",
                         pp = "pp",
                         ToD_int = c(5, 13, 18, 23))

rm(bp_hypnos)

dow_tod_plots_out <- dow_tod_plots(hyp_proc)
grid::grid.draw(
   gridExtra::grid.arrange(dow_tod_plots_out[[1]], dow_tod_plots_out[[2]], ncol = 2)
 )

Compatibility Check for path Argument

Description

Compatibility Check for path Argument

Usage

path_check(path)

Arguments

Value

A formatted string corresponding to the proper binding of directory and base naming conventions. If user's path is invalid, path_check will throw an error.

Examples

## Not run: 
path = "~dir/abpm/"
path_check(path) # drops the trailing slash

## End(Not run)

Data Pre-Processor

Description

A helper function to assist in pre-processing the user-supplied input data in a standardized format for use with other functions in the bp package. See Vignette for further details.

Usage

process_data(
  data,
  bp_type = c("hbpm", "abpm", "ap"),
  ap = NULL,
  time_elap = NULL,
  sbp = NULL,
  dbp = NULL,
  date_time = NULL,
  id = NULL,
  group = NULL,
  wake = NULL,
  visit = NULL,
  hr = NULL,
  pp = NULL,
  map = NULL,
  rpp = NULL,
  DoW = NULL,
  ToD_int = NULL,
  eod = NULL,
  data_screen = TRUE,
  SUL = 240,
  SLL = 50,
  DUL = 140,
  DLL = 40,
  HRUL = 220,
  HRLL = 27,
  inc_low = TRUE,
  inc_crisis = TRUE,
  agg = FALSE,
  agg_thresh = 3,
  collapse_df = FALSE,
  dt_fmt = "ymd HMS",
  chron_order = FALSE,
  tz = "UTC"
)

Arguments

Value

A processed dataframe object with standardized column names and formats to use with the rest of bp package functions. The following standardized column names are used throughout

References

Lee H, Yano Y, Cho SMJ, Park JH, Park S, Lloyd-Jones DM, Kim HC. Cardiovascular risk of isolated systolic or diastolic hypertension in young adults. Circulation. 2020; 141:1778–1786. doi: 10.1161/CIRCULATIONAHA.119.044838

Omboni, S., Parati, G*., Zanchetti, A., Mancia, G. Calculation of trough: peak ratio of antihypertensive treatment from ambulatory blood pressure: methodological aspects Journal of Hypertension. October 1995 - Volume 13 - Issue 10 - p 1105-1112 doi: 10.1097/00004872-199510000-00005

Unger, T., Borghi, C., Charchar, F., Khan, N. A., Poulter, N. R., Prabhakaran, D., ... & Schutte, A. E. (2020). 2020 International Society of Hypertension global hypertension practice guidelines. Hypertension, 75(6), 1334-1357. doi: 10.1161/HYPERTENSIONAHA.120.15026

Examples

# Load bp_hypnos
data("bp_hypnos")

# Process data for bp_hypnos
hypnos_proc <- process_data(bp_hypnos,
                              bp_type = 'abpm',
                              sbp = 'syst',
                              dbp = 'diast',
                              date_time = 'date.time',
                              hr = 'hr',
                              pp = 'PP',
                              map = 'MaP',
                              rpp = 'Rpp',
                              id = 'id',
                              visit = 'Visit',
                              wake = 'wake',
                              data_screen = FALSE)

hypnos_proc


# Load bp_jhs data
data("bp_jhs")

# Process data for bp_jhs
# Note that bp_type defaults to "hbpm" and is therefore not specified
jhs_proc <- process_data(bp_jhs,
                         sbp = "Sys.mmHg.",
                         dbp = "Dias.mmHg.",
                         date_time = "DateTime",
                         hr = "Pulse.bpm.")

jhs_proc

Sleep Interval Manual Override

Description

Adjusts WAKE column to reflect start and end of user-defined sleep period. If sleep_start_end is NULL and WAKE column is included in input data, this function will not alter data.

Usage

sleep_int(data, sleep_start_end = NULL, adj = TRUE)

Arguments

Value

Dataframe with added / adjusted WAKE column moved after DATE_TIME column containing binary 1 (awake) and 0 (asleep)

Compatibility Checks for User-Supplied SBP/DBP Stages Vector

Description

Used in process_data function

Usage

stage_check(sbp_stages = NULL, dbp_stages = NULL)

Arguments

Value

A list containing two vectors that each correspond to the SBP/DBP stages for use in further processing. If the function returns no error, the supplied vectors are valid. Function arguments are coerced to integer.

Compatibility Check for User-Supplied Subject Subset Vector

Description

Compatibility Check for User-Supplied Subject Subset Vector

Usage

subject_subset_check(data, subj = NULL)

Arguments

Value

Logical indicating whether the subj vector passes the checks (TRUE), or not (FALSE)

Successive Variation (SV)

Description

THIS IS A DEPRECATED FUNCTION. USE bp_sv INSTEAD.

Usage

sv(
  data,
  inc_date = FALSE,
  subj = NULL,
  bp_type = 0,
  add_groups = NULL,
  inc_wake = TRUE
)

Arguments

Details

Calculate the successive variation (SV) at various levels of granularity based on what is supplied (ID, VISIT, WAKE, and / or DATE)for either SBP, DBP, or both. SV is a measure of dispersion that takes into account the temporal structure of the data and relies on the sum of squared differences in successive observations, unlike the average real variability (ARV) which relies on the sum of absolute differences. $$SV = sqrt(sum(x_i+1 - x_i)^2/n-1)$$

NOTE: The canonical standard deviation, independent of the temporal structure using the sample average, is added for comparison: $$SD = sqrt(sum(x_i+1 - xbar)^2/n-1)$$

Value

A tibble object with a row corresponding to each subject, or alternatively a row corresponding to each date if inc_date = TRUE. The resulting tibble consists of:

• ID: The unique identifier of the subject. For single-subject datasets, ID = 1

• VISIT: (If applicable) Corresponds to the visit # of the subject, if more than 1

• WAKE: (If applicable) Corresponds to the awake status of the subject (0 = asleep | 1 = awake)

• SV_SBP / SV_DBP: Calculates the square root of the average squared differences between successive measurements. The resulting value averages across the granularity grouping for however many observations are present.

• N: The number of observations for that particular grouping. If inc_date = TRUE, N corresponds to the number of observations for that date. If inc_date = FALSE, N corresponds to the number of observations for the most granular grouping available (i.e. a combination of ID, VISIT, and WAKE)

• Any add_groups variables supplied to function argument will be present as a column in the resulting tibble.

Examples

# Load data
data(bp_hypnos)
data(bp_jhs)

# Process bp_hypnos
hypnos_proc <- process_data(bp_hypnos, sbp = "SYST", dbp = "DIAST", date_time = "date.time",
id = "id", wake = "wake", visit = "visit", hr = "hr", pp ="pp", map = "map", rpp = "rpp")
# Process bp_jhs data
jhs_proc <- process_data(bp_jhs, sbp = "Sys.mmHg.", dbp = "Dias.mmHg.", date_time = "DateTime",
hr = "Pulse.bpm.")

# SV Calculation
## Not run: 
bp_sv(hypnos_proc)
bp_sv(jhs_proc, add_groups = c("meal_time"))
# Notice that meal_time is not a column from process_data, but it still works

## End(Not run)