In awconway/nc-mscs: Nursing Confidence in Managing Sedation Complications Scale

Introduction

Prompt detection and initiation of appropriate treatment for sedation-related complications is vital to ensure patient safety during nurse-administered procedural sedation and analgesia (PSA). People will exert maximal effort and persist despite failure if they believe they are capable in completing a given task. It is possible that nurses may not take necessary actions to manage sedation-related complications if they lack confidence in their abilities to complete the task. The aim of this study was to develop and validate the nursing confidence in managing sedation complications scale (NCMSCS).

Methods

A draft version of the NC-MSCS was developed from clinical practice guidelines. An expert panel of nurses and medical practitioners who have expertise in anaesthesia or PSA were asked to rate the relevance of each item. Items with a content validity index of less than 0.78 were revised and those with very low index values were deleted. To evaluate the psychometric properties of the revised scale, an online survey was distributed to nurses who were members of operating theatre, endoscopy, cardiac cath lab, radiology and critical care specialty nursing associations. Exploratory factor analysis was undertaken to provide evidence of the structural validity of the scale.

Load data

load("data/data.Rdata")

Descriptive statistics for the sample

library(psych)
data$GSES=rowMeans(data[,14:21], na.rm = TRUE)
variables=c(7:9,22:23,58)
kableExtra::kable(describe(data[,variables]), caption="Descriptive statistics")
##need to also add in factor variable descriptives.

Missing data analysis

df=data[,24:57]

The percent of missing data:

(sum(is.na(df))/prod(dim(df)))*100

Impute with expectation maximisation

library(TestDataImputation)
df1=EMimpute(df, Mvalue = "NA", max.score = 7)

Prepare dataframe for exploratory factor analysis Delete items: Sedation reversal – worded with more than one part in the question Laryngeal mask airway – not all trained for this AssessLevel Sedation - wording was too similar to identify over-sedation

df1$Medication=NULL
df1$LMA=NULL
df1$AssessLevelSedation=NULL

describe(df1)

summary(df1)

Box plots of item distributions from https://towardsdatascience.com/a-gentle-guide-to-statistics-in-r-ccb91cc1177e

library(tidyr)
#create long dataframe
dflong=gather(df1,key=Item, value=Confidence)
#Change item names
library(plyr)
dflong$Item=mapvalues(dflong$Item, from = c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp",
"IdentifyHypoventilation", "IdentifyRespRate"    ,    "IdentifyAllergy"  ,
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension" ,
"NontechAssess"  ,"NontechAccessAssist","NontechRespond",
"OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift", "BMV", "IdentifyLaryngospasm", "IdentifyObstruction", "IdentifyOversedation", "NontechIdentifyAssist", "RespondObstruction", "RiskComorbid", "RiskFrailty"), to = c("Assess risk from anaesthetic history" ,        "Assess risk from sleep apnoea"       ,          "Determine risk for difficult intubation"    ,   "Determine risk for difficult bag mask ventilation"           
,"Assess risk from ASA classification status"        ,            "Assess risk from body mass index"          ,       "Assess risk from cardiorespiratory reserve",
"Identify hypoventilation", "Identify abnormal respiratory rate"    ,    "Identify allergic reactions"  ,
 "Identify bradycardia"   ,  "Identify hypotension"     ,         "Respond to hypoventilation" ,
 "Respond to hypoxia"      ,      "Respond to bradycardia"    ,  "Respond to hypotension" ,
"Cease procedure to assess patient"  ,"Cease procedure to gain extra assistance","Cease procedure to treat complication",
"Insert oropharyngeal airway"     ,                "Insert nasopharyngeal airway"          ,           "Apply jaw support"  ,           
"Apply chin lift", "Perform bag-mask ventilation", "Identify laryngospasm", "Identify airway obstruction", "Identify oversedation", "Identify when extra assistance required", "Respond to airway obstruction", "Assess risk from comorbidities", "Assess risk from frailty assessment"))
library(ggplot2)
ggplot(data = dflong, # add the data
       aes(x = Item, y = Confidence, # set x, y coordinates
           color = Item)) +    # color by item
  geom_boxplot() +
theme(axis.ticks = element_blank(), axis.text.x = element_blank())

Test factorability using KMO

KMO(df1)

Determine Number of Factors to Extract

library(nFactors)
polycor=polychoric(df1, smooth = FALSE)
ev <- eigen(cor(polycor$rho))
ap <- parallel(subject=nrow(polycor$rho),var=ncol(polycor$rho),
  rep=100,cent=.05, model="factors")
nS <- nScree(x=ev$values, aparallel=ap$eigen$qevpea)
plotnScree(nS)

Exploratory factor analysis

Trial of 3 factors

df3=df1
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Conceptualise factor structure as: 1. a factor for assessing risk of and identification of airway complications 2. A factor for treating airway complications 3. Other complications

Remove RiskComorbidity because difference <0.15

df3$RiskComorbid=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove IdentifyHypoventilation becuase difference <0.15

df3$IdentifyHypoventilation=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove BMV as difference <0.15

df3$BMV=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove RespondObstruction because difference <0.15

df3$RespondObstruction=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove RiskFrailty because it loaded on the wrong factor

df3$RiskFrailty=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove NontechAccessAssist because of Heywood case

df3$NontechAccessAssist=NULL
polycor=polychoric(df3, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

This solution identified a factor structure consisting of: 1. Advanced airway assessment skills 2. Technical skills related to airway patency 3. General skills related to identifying and responding to sedation-related complications.

Create scale and subscale scores for 3 factor solution

MSCS.keys.3=list(advanced=c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp", "IdentifyLaryngospasm", "IdentifyObstruction"),
general=c("IdentifyRespRate"    ,    "IdentifyAllergy"  , "IdentifyOversedation",
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension" ,"NontechAssess"  ,"NontechIdentifyAssist","NontechRespond"  ),
airway=c("OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift" ),
total=c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp", "IdentifyLaryngospasm", "IdentifyObstruction", "IdentifyOversedation",
 "IdentifyRespRate"    ,    "IdentifyAllergy"  ,
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension" ,
"NontechAssess"  ,"NontechAccessAssist","NontechRespond",
"OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift"))

my.scales.3=scoreItems(MSCS.keys.3, df1)
my.scores.3=my.scales.3$scores

Add scale scores to data frame

data$advanced=my.scores.3[,1]
data$general=my.scores.3[,2]
data$airway=my.scores.3[,3]
data$total.3=my.scores.3[,4]

Describe scale scores

plyr::count(data$advanced)
plyr::count(data$airway)
plyr::count(data$general)
plyr::count(data$total.3)

Start of alternative 3 factor solution: Remove RiskFrailty because it loaded on the wrong factor

df3alt=df1
df3alt$RiskFrailty=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove RiskComorbid because difference <0.15

df3alt$RiskComorbid=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove IdentifyLaryngospasm because it loaded on the wrong factor

df3alt$IdentifyLaryngospasm=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove IdentifyObstruction because it loaded on the wrong factor

df3alt$IdentifyObstruction=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove IdentifyHypoventilation because difference was <0.15

df3alt$IdentifyHypoventilation=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Remove RespondHypoventilation because difference was <0.15

df3alt$RespondHypoventilation=NULL
polycor=polychoric(df3alt, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 3, rotate = "Promax", fm="ols")
factor

Trial of 4 factor structure

df4=df1
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove IdentifyLaryngospasm because it loaded highly on the wrong factor

df4$IdentifyLaryngospasm=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove RiskFrailty because it loaded on wrong factor*

df4$RiskFrailty=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove RespondObstruction – loaded on more than one factor with difference <0.15*

df4$RespondObstruction=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove RiskComorbidity because it loaded highly on two factors with difference <0.15 between factors*

df4$RiskComorbid=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove Identifyobstruction – loaded on wrong factor*

df4$IdentifyObstruction=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove NontechIdentifyAssist because loaded highly on two factors with <0.15 difference*

df4$NontechIdentifyAssist=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove BMV loaded less than 0.4 on all factors*

df4$BMV=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "Promax", fm="ols")
factor

Remove IdentifyOversedation loaded less than 0.4 on all factors*

df4$IdentifyOversedation=NULL
polycor=polychoric(df4, smooth = FALSE)
factor=fa(polycor$rho, nfactors = 4, rotate = "promax", fm="ols")

print(factor)

Create scale and subscale scores

MSCS.keys=list(risk=c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp"),
identify=c("IdentifyHypoventilation", "IdentifyRespRate"    ,    "IdentifyAllergy"  ,
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension"   ),
nontech=c("NontechAssess"  ,"NontechAccessAssist","NontechRespond" ),
tech=c("OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift" ),
total=c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp",
"IdentifyHypoventilation", "IdentifyRespRate"    ,    "IdentifyAllergy"  ,
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension" ,
"NontechAssess"  ,"NontechAccessAssist","NontechRespond",
"OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift"))

my.scales=scoreItems(MSCS.keys, df1)
my.scores=my.scales$scores

Add scale scores to data frame

data$risk=my.scores[,1]
data$identify=my.scores[,2]
data$nontech=my.scores[,3]
data$tech=my.scores[,4]
data$total=my.scores[,5]

Describe scale scores

plyr::count(data$tech)
plyr::count(data$nontech)
plyr::count(data$risk)
plyr::count(data$identify)
plyr::count(data$total)
plyr::count(data$OverallConfidence)
describe(data[,58:62])

Plot from: https://rpubs.com/danmirman/plotting_factor_analysis

t=as.table(print(factor$loadings))
t1=as.data.frame(t)
library(plyr)
t1$Var2=mapvalues(t1$Var2, from = c("A", "B", "C", "D"), to = c("Identify and respond", "Risk assessment", "Technical skills", "Non-technical skills"))
t1$Var1=mapvalues(t1$Var1, from = c("RiskHistory" ,        "RiskOSA"       ,          "DifficultIntubate"    ,   "DifficultBMV"           
,"RiskASA"        ,            "RiskBMI"          ,       "RiskCardioresp",
"IdentifyHypoventilation", "IdentifyRespRate"    ,    "IdentifyAllergy"  ,
 "IdentifyBradycardia"   ,  "IdentifyHypotension"     ,         "RespondHypoventilation" ,
 "RespondHypoxia"      ,      "RespondBradycardia"    ,  "RespondHypotension" ,
"NontechAssess"  ,"NontechAccessAssist","NontechRespond",
"OPA"     ,                "NPA"          ,           "JawSupport"  ,           
"ChinLift"), to = c("Assess risk from anaesthetic history" ,        "Assess risk from sleep apnoea"       ,          "Determine risk for difficult intubation"    ,   "Determine risk for difficult bag mask ventilation"           
,"Assess risk from ASA classification status"        ,            "Assess risk from body mass index"          ,       "Assess risk from cardiorespiratory reserve",
"Identify hypoventilation", "Identify abnormal respiratory rate"    ,    "Identify allergic reactions"  ,
 "Identify bradycardia"   ,  "Identify hypotension"     ,         "Respond to hypoventilation" ,
 "Respond to hypoxia"      ,      "Respond to bradycardia"    ,  "Respond to hypotension" ,
"Cease procedure to assess patient"  ,"Cease procedure to gain extra assistance","Cease procedure to treat complication",
"Insert oropharyngeal airway"     ,                "Insert nasopharyngeal airway"          ,           "Apply jaw support"  ,           
"Apply chin lift"))
names=c("Item", "Factor", "Loading")
colnames(t1)=names

library(ggplot2)
#For each test, plot the loading as length and fill color of a bar
# note that the length will be the absolute value of the loading but the 
# fill color will be the signed value, more on this below

ggplot(t1, aes(Item, abs(Loading), fill=Loading)) + 
  facet_wrap(~ Factor, nrow=1) + #place the factors in separate facets
  geom_bar(stat="identity") + #make the bars
  coord_flip() + #flip the axes so the test names can be horizontal  
  #define the fill color gradient: blue=positive, red=negative
  scale_fill_gradient2(name = "Loading", 
                       high = "blue", mid = "white", low = "red", 
                       midpoint=0, guide=F) +
  ylab("Loading Strength") + #improve y-axis label
  theme_bw(base_size=10) #use a black and white theme with set font size

Test construct validity through multiple regression of factors potentially associated with confidence in managing sedation-related complications

Create total general self efficacy scale score

data$GSE=rowSums(data[,14:21], na.rm = FALSE)
head(data$GSE)

Create critical care dummy variable

data$critcare=data$Specialty==10|data$Specialty==9|data$Specialty==8|data$Specialty==7

Load the broom, dplyr and kableExtra packages

library(broom)
library(kableExtra)
library(dplyr)

Model for total score

model=lm(total~YearsNursing+ PSAFrequency+ YearsPSA+ ALS+ PSAEducation+ GSE+ critcare+ PSAPolicy+ Competency+ OverallKnowledge, data)
kable(tidy(model, conf.int=TRUE))

Plot

ggplot(tidy(model, conf.int=TRUE), aes(estimate, term, color = term)) +
  geom_point() +
  geom_errorbarh(aes(xmin = conf.low, xmax = conf.high)) +
  geom_vline(aes(xintercept=0))+
  scale_y_discrete(labels = c("(Intercept)", "ALS", "Unit has a PSA Competency", "Critical care", "General self-efficacy", "Sedation knowledge rating", "Education", "Frequency using PSA", "Unit has a PSA Policy", "Years Nursing", "Years administering sedation")) +
  theme(legend.position="none")+
  labs(y = "Predictor", x="Estimate") + 
  theme(axis.title.y = element_text(size = 14, face = "bold"), axis.title.x = element_text(size = 14, face = "bold"))+
    theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank(),
        panel.background = element_blank())

ggcoefstats plot

https://cran.r-project.org/web/packages/ggstatsplot/vignettes/ggcoefstats.html

Model for risk assessment scale

model=lm(data$risk~data$YearsNursing+data$PSAFrequency+data$YearsPSA+data$ALS+data$PSAEducation+data$GSE+data$critcare+data$PSAPolicy+data$Competency + data$OverallKnowledge)
summary(model)

Model for identifying and responding to complications scale

model=lm(data$identify~data$YearsNursing+data$PSAFrequency+data$YearsPSA+data$ALS+data$PSAEducation+data$GSE+data$critcare+data$PSAPolicy+data$Competency+ data$OverallKnowledge)
summary(model)

Model for technical skills scale

model=lm(data$tech~data$YearsNursing+data$PSAFrequency+data$YearsPSA+data$ALS+data$PSAEducation+data$GSE+data$critcare+data$PSAPolicy+data$Competency+ data$OverallKnowledge)
summary(model)

Model for non-technical skills scale

model=lm(data$nontech~data$YearsNursing+data$PSAFrequency+data$YearsPSA+data$ALS+data$PSAEducation+data$GSE+data$critcare+data$PSAPolicy+data$Competency+ data$OverallKnowledge)
summary(model)

consider doing plots of multiple regressions using ggstatsplot:: ggcoefstats https://cran.r-project.org/web/packages/ggstatsplot/vignettes/ggcoefstats.html

plots for associations between total scores and explanatory variables

ggplotRegression <- function (fit) {

  library(ggplot2)

  ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) + 
    geom_point() +
    theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
    stat_smooth(method = "lm", col = "red") +
    labs(title = paste("Adj R squared = ",signif(summary(fit)$adj.r.squared, 5),
                       "Intercept =",signif(fit$coef[[1]],5 ),
                       " Slope =",signif(fit$coef[[2]], 5),
                       " P =",signif(summary(fit)$coef[2,4], 5)))
}

ggplotRegression(lm(total ~ OverallConfidence, data = data))
ggplotRegression(lm(total ~ OverallKnowledge, data = data))
ggplotRegression(lm(total ~ YearsPSA, data = data))
ggplotRegression(lm(total ~ YearsNursing, data = data))
ggplotRegression(lm(total ~ PSAFrequency, data = data))
ggplotRegression(lm(total ~ GSE, data = data))

Reliability analysis

reliability.total=psych::alpha(df1)$total$std.alpha

dfnontech=data.frame(data$NontechAccessAssist, data$NontechRespond, data$NontechAssess)
reliability.nontech=psych::alpha(dfnontech)$total$std.alpha

dftech=data.frame(data$ChinLift, data$JawSupport, data$NPA, data$OPA)
reliabilitytech=psych::alpha(dftech)$total$std.alpha

dfrisk=data.frame(df1[,1:7])
reliabilityrisk=psych::alpha(dfrisk)$total$std.alpha

dfidentify=data.frame(df1[,c(8:12,14:17)])
reliability.identify=psych::alpha(dfidentify)$total$std.alpha
data.frame(reliability.total,reliability.identify,reliability.nontech, reliabilitytech, reliabilityrisk)

Responsiveness

Load data

responsiveness <- readr::read_csv("data/responsiveness.csv")

Create pre NC-MSCS scores

Print variable pre-test variable names

names.list=names(responsiveness)

Print variable pre-test variable names

variablenames.mscsPost=dput(grep(".post$", names(responsiveness), value=TRUE))
names.list[29:30]

pre.keys=list(risk=c("Risk_anaesthesia_pre", "Risk_OSA_pre" ,          "Risk_intubate_pre"     ,     "Risk_BMV_pre"       ,        "Risk_ASA_pre" ,    "Risk_BMI_pre"         ,      "Risk_cardiorespiratory_pre"),
identify=c("Identify_hypoventilation_pre", "Identify_resprate_pre"     ,   "Identify_allergic_pre"     ,   "Identify_sedationlevel_pre" ,"Respond_hypoventilation_pre", "Respond_hypoxia_pre","Respond_bradycardia_pre", "Respond_hypotension_pre" ),
tech=c("Intervention_OPA_pre"     ,                "Intervention_NPA_pre"          ,           "Intervention_jawsupport_pre"  , 
"Intervention_chinlift_pre" ),
nontech=c("Nontech_access_assistance_pre", "Nontech_respond_pre", "Nontech_assessment_pre"),
total=c("Risk_OSA_pre" ,          "Risk_intubate_pre"     ,     "Risk_BMV_pre"       ,        "Risk_ASA_pre" ,"Risk_comorbidities_pre" ,    "Risk_BMI_pre"         ,      "Risk_cardiorespiratory_pre","Identify_hypoventilation_pre", "Identify_resprate_pre"     ,   "Identify_allergic_pre"     ,   "Identify_sedationlevel_pre" ,"Respond_hypoventilation_pre", "Respond_hypoxia_pre","Respond_bradycardia_pre", "Respond_hypotension_pre","Intervention_OPA_pre"     ,                "Intervention_NPA_pre"          ,           "Intervention_jawsupport_pre"  , 
"Intervention_chinlift_pre" ,"Nontech_access_assistance_pre", "Nontech_respond_pre", "Nontech_assessment_pre"))

post.keys=list(risk=c("Risk_anaesthesia_post","Risk_OSA_post" ,          "Risk_intubate_post"     ,     "Risk_BMV_post"       ,        "Risk_ASA_post" ,    "Risk_BMI_post"         ,      "Risk_cardiorespiratory_post"),
identify=c("Identify_hypoventilation_post", "Identify_resprate_post"     ,   "Identify_allergic_post"     ,   "Identify_sedationlevel_post" ,"Respond_hypoventilation_post", "Respond_hypoxia_post","Respond_bradycardia_post", "Respond_hypotension_post" ),
tech=c("Intervention_OPA_post"     ,                "Intervention_NPA_post"          ,           "Intervention_jawsupport_post"  , 
"Intervention_chinlift_post" ),
nontech=c("Nontech_access_assistance_post", "Nontech_respond_post", "Nontech_assessment_post"),
total=c("Risk_OSA_post" ,          "Risk_intubate_post"     ,     "Risk_BMV_post"       ,        "Risk_ASA_post" ,"Risk_comorbidities_post" ,    "Risk_BMI_post"         ,      "Risk_cardiorespiratory_post","Identify_hypoventilation_post", "Identify_resprate_post"     ,   "Identify_allergic_post"     ,   "Identify_sedationlevel_post" ,"Respond_hypoventilation_post", "Respond_hypoxia_post","Respond_bradycardia_post", "Respond_hypotension_post","Intervention_OPA_post"     ,                "Intervention_NPA_post"          ,           "Intervention_jawsupport_post"  , 
"Intervention_chinlift_post" ,"Nontech_access_assistance_post", "Nontech_respond_post", "Nontech_assessment_post"))

pre.scales=scoreItems(pre.keys, responsiveness)
pre.scores=pre.scales$scores
post.scales=scoreItems(post.keys, responsiveness)
post.scores=post.scales$scores

Add scale scores to data frame

responsiveness$pre.risk=pre.scores[,1]
responsiveness$pre.identify=pre.scores[,2]
responsiveness$pre.nontech=pre.scores[,3]
responsiveness$pre.tech=pre.scores[,4]
responsiveness$pre.total=pre.scores[,5]

responsiveness$post.risk=post.scores[,1]
responsiveness$post.identify=post.scores[,2]
responsiveness$post.nontech=post.scores[,3]
responsiveness$post.tech=post.scores[,4]
responsiveness$post.total=post.scores[,5]

Describe scale scores

plyr::count(responsiveness$pre.tech)
plyr::count(responsiveness$pre.nontech)
plyr::count(responsiveness$pre.risk)
plyr::count(responsiveness$pre.identify)
plyr::count(responsiveness$pre.total)
plyr::count(responsiveness$PSAconfidence_pre)

plyr::count(responsiveness$post.tech)
plyr::count(responsiveness$post.nontech)
plyr::count(responsiveness$post.risk)
plyr::count(responsiveness$post.identify)
plyr::count(responsiveness$post.total)
plyr::count(responsiveness$PSAconfidence_post)
responsiveness$totalchange=responsiveness$post.total- responsiveness$pre.total
responsiveness$Confidence_change=responsiveness$PSAconfidence_post - responsiveness$PSAconfidence_pre
describe(responsiveness[,73:83])

library(psychometric)

sd=sd(responsiveness$totalchange, na.rm=TRUE)
meanchange=mean(responsiveness$totalchange, na.rm=TRUE)
SRM=meanchange/sd
SRM
GRI=meanchange/sd(responsiveness$pre.total, na.rm=TRUE)
GRI

Plots for paired data

new=na.omit(data.frame(responsiveness$pre.total,responsiveness$post.total))
columns2=c("before", "after")
colnames(new)=columns2
totalafter=new$after
totalbefore=new$before

risk=na.omit(data.frame(responsiveness$pre.risk,responsiveness$post.risk))
columns2=c("before", "after")
colnames(risk)=columns2
riskafter=risk$after
riskbefore=risk$before

tech=na.omit(data.frame(responsiveness$pre.tech,responsiveness$post.tech))
columns2=c("before", "after")
colnames(tech)=columns2
techafter=tech$after
techbefore=tech$before

library(PairedData)
total <- paired(totalbefore, totalafter)
plot(total, type = "profile") + theme_bw() +ggtitle("NC-MSCS scores before and after training in sedation") + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

risk <- paired(riskbefore, riskafter)
plot(risk, type = "profile") + theme_bw() +ggtitle("Risk subscale scores before and after training in sedation") + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

tech <- paired(techbefore, techafter)
plot(tech, type = "profile") + theme_bw() +ggtitle("Technical skills subscale scores before and after training in sedation") + theme_bw() + theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))

1. Risk

Plot of correlations

ggplotRegression(lm(totalchange ~ Confidence_change, data = responsiveness))

awconway/nc-mscs documentation built on Feb. 17, 2021, 10:07 p.m.