Needle Angle Matters: An Investigation of the Effect of Needle Angle on Hemodialysis Cannulation Skill

Abstract

Key Points

• Three metrics that quantify cannulation skill on the basis of needle angle are introduced.

• All three needle angle metrics were demonstrated to be useful in predicting cannulation outcomes on the simulator.

Background

Cannulation is critical for maintaining a functional vascular access for patients on hemodialysis. However, relatively little is known about the quantitative aspects of needle insertion dynamics required for skilled cannulation. In this article, we introduce three kinds of metrics that quantify needle insertion angle—recognized as important for safe and effective cannulation—during cannulation on an instrumented simulator for skill assessment. Three questions were examined in this study: (1) Are simulator-based needle angle metrics related to cannulation success? (2) Are needle angle metrics related to simulated blood flashback quality? and (3) Can needle angle metrics be used to distinguish between high and low skill levels?

Methods

Fifty-one cannulators with varying degrees of clinical experience performed cannulation on the instrumented simulator. Each participant cannulated 16 times on different fistulas with varying geometries. During each trial, needle angle along with other sensor data was obtained through a motion sensor placed inside the needle. Data analysis was conducted by relating needle angle over time with our previously validated simulator-based cannulation outcome metrics.

Results

The results revealed that all three types of needle angle metrics were useful in predicting the probability of cannulation success. In addition, they were also correlated with flashback quality metrics. Furthermore, these metrics successfully distinguished between high and low performers regardless of whether they were classified using subjective ratings or objective scores. These results indicate that needle insertion angle is an important component of cannulation skill.

Conclusions

The simulator-based metrics for needle insertion angle presented in this work measure a key aspect of skilled cannulation. As such, if implemented in a structured way, these metrics could lead to competency-based skill assessment and training for cannulation in the future. Raising the bar of cannulation skill of our clinicians can have a tangible effect on patient outcomes.

Introduction

Hemodialysis (HD) is the single most prevalent treatment for patients with end-stage renal disease in the United States. Longevity on dialysis is proportional to the quality of dialysis, which depends on the reliability and integrity of a patient's vascular access (VA).¹ Currently, the use of an arteriovenous (AV) fistula is the preferred VA for HD because of its low rate of complications, lower morbidity, and fewer maintenance costs.² However, suboptimal needle cannulation can still cause complications, including major infiltration and hematoma formation,³ and may also increase a patients' anxiety and fear.⁴ Therefore, to ensure quality patient outcomes, cannulation skills are essential for dialysis nurses and technicians.

However, current methods for cannulation skills training are both antiquated and lacking. “Fake arm” manikins are routinely used in the initial orientation of trainee dialysis patient care technicians (PCTs), but these educational tools have limited value in realism and capacity for providing feedback. In addition, trainees are often provided “on the job” instruction by senior PCTs or nurses, but this mode of teaching is primarily subjective, unstructured, and nonstandardized, leading to inferior cannulation outcomes.^5–7 Given these limitations, the dialysis community recognizes the need to address this significant issue of the lack of cannulation skills training.

One way to address the need for structured, competency-based cannulation skills training is to identify best practices for cannulation. The recently updated Kidney Disease Outcomes Quality Initiative guidelines include specific guidance for cannulation of VAs for HD; for example, needle insertion angle has been prescribed to be around 25° during cannulation.⁸ However, measuring needle angle during cannulation has thus far not been systematically studied nor has the effect of needle angle on cannulation outcomes.

In the recent decade, simulators have garnered a prominent role in medical education because of their ability to provide training in a nonclinical environment with objective, sensor-based feedback. As such, simulators have been used to provide standardized assessment and credentialing for several medical specialties (e.g., general surgery). Specific to VA training, simulation training has been shown to shorten learning time and provide a safe learning environment.⁹ Dunbar-Reid and colleagues created a dialysis-specific simulation model that allowed for the rehearsal of a variety of scenarios that may arise during HD, for example, cardiac arrest.¹⁰ Other educators have called for the design and use of more advanced VA simulators beyond using traditional animal models (e.g., turkey leg).¹¹ In line with this, our team has developed a “smart” cannulation simulator with the ability to comprehensively quantify cannulation skill.^12–17

In this work—and to the best of our knowledge, for the first time—we present a simulator-based, quantitative study examining the relationship between needle angles, cannulation outcomes, and cannulation skill from a cohort of nurses and PCTs. Toward this, we introduce three kinds of novel angle metrics on the basis of needle insertion angle profiles during cannulation to quantify cannulation performance. Furthermore, we also examine angle-based needle behavior in different portions of the cannulation procedure for the whole cannulation attempt and during segments (i.e., portions) of cannulation attempts. Consequently, we studied the performance on three segments: the whole attempt, before flashback (“flashback” is the colloquially used clinical term for blood visible to the nurse/PCT as it enters the cannula), and after flashback. Several cannulation outcome metrics on the basis of flashback were calculated during needle insertion attempts. All the metrics were measured using our customized sensor-based dialysis cannulation simulator.

This study addressed three questions pertaining to needle angle behavior during cannulation on the simulator:

(Q1) Are needle angle metrics related to cannulation success? (Q2) Are needle angle metrics related to flashback quality? (Q3) Can needle angle metrics be used to distinguish between high and low performance (i.e., skill) levels?

Methods

Cannulation Simulator

An experimental study was conducted on a custom-made simulator designed specifically for HD cannulation in consultation with expert clinicians over 5 years.¹² The simulator featured four artificial fistulas of varying geometries inside a bed; in addition, needle and hand motion and force were measured using sensors as described previously¹² (see Figure 1). The four artificial fistulas were fabricated using cured silicone with both curved and straight geometries and depth of 5 mm (see Figure 1). The realism of the simulators was extensively tested by our clinical collaborators. There was no liquid present in the simulator; rather, infrared sensors were used to track whether the needle tip (a 14 gauge, 1 in needle) entered the fistula. On puncturing the fistula, a red light emitting diode inside the cannula was lit to simulate blood flashback. Owing to the design, the flashback is not dependent on blood hemodynamics, for example, in the case of stenosis. Furthermore, the design of the fistulas allowed for a realistic “pop” sensation on needle puncture. A miniature vibration motor embedded inside the fistula allowed for providing the haptic feel of “thrill” characteristic of AV fistulas. For each cannulation trial, only one of the four fistulas was randomly activated with its haptic “thrill” feedback.

Figure 1.

An illustration of the cannulation simulator,⁹ designed fistulas, the stages of needle insertion, and example profiles of needle insertion angle from high and low performers during cannulation. Red asterisks in the figure show the points of angle reversals. $\overline{\alpha }$, average insertion angle.

Participants and Experimental Protocol

This study, approved by Clemson University's Institutional Review Board, included 51 nurses and dialysis technicians, who voluntarily enlisted in this study. Twenty-nine nurses and 19 dialysis technicians (n=44 female) with an average clinical experience of 12 years participated in this study.¹⁷ Before the experiment, participants completed a questionnaire on their clinical experience as well as viewed a presentation detailing the experimental protocol. The experimental task comprised cannulating each of four artificial fistulas on the simulator with varying geometries (four times each in a random order) with the goal of successfully obtaining simulated blood flashback (indicated via a red light emitting diode inside the cannula). Each participant performed 16 cannulation trials on the simulator. Participants had no limits on time or the number of attempts and could terminate the cannulation trial without obtaining flashback. During the experiment, one of four expert observers graded a subject's overall performance after 16 trials (score range: 1–7) using a Global Rating Scale (GRS) sheet (see Figure 4 in our previous study¹⁷) that included a participant's performance of palpation, needle holding, movement, and blood flashback quality during cannulation.

Figure 4.

The boxplots of angle-based metrics of 11 HPs and 12 LPs on three segments. (A) Angle-based metrics of two groups defined by overall GRS score. (B) Angle-based metrics of two groups defined by average ocScore. *P value < 0.05. $\overline{\alpha }$, average insertion angle; HP, high performer; #angle_changes, number of times the needle angle is changed; LDLJ(α), log dimensionless jerk of insertion angle; LP, low performer; S, whole attempt; s1, before flashback; s2, after flashback.

Needle Angle Metrics

Needle angle during cannulation was calculated based on the needle's orientation with respect to the artificial skin's surface plane (see Supplemental Figures 1 and 2 in Supplemental Material). Three kinds of angle metrics on the basis of the angle time series profile during needle insertion were proposed and used in this study: the average insertion angle ($\overline{\alpha }$); the number of times the needle angle is changed during cannulation (#angle_changes), which denotes needle “digging”; and the log dimensionless jerk of insertion angle (LDLJ(α)), which evaluates “smoothness” of needle angle.¹³ The details of the calculation of angle metrics are presented in Supplemental Table 1 of the Supplemental Material. Our previous study demonstrated that different segments of the cannulation task elicit segment-specific behavior.¹⁸ For instance, after obtaining flashback, participants lowered the angle of needle to avoid infiltration. Consequently, to better quantify the insertion performance during an attempt, the angle metrics were extracted at three segments: the whole attempt (S), before receiving flashback (s₁), and after receiving flashback (s₂). If there was no flashback, the angle metrics before flashback (s₁) and after flashback (s₂) were set to not applicable; consequently, these metrics were excluded from the analysis. Two example profiles of needle insertion angle during cannulation representing high and low performers (LPs) are shown in Figure 1.

Flashback-Based Outcome Metrics

Two kinds of outcome metrics were used to quantify the quality of blood flashback when cannulating on the simulator. Cannulation success (success) was defined as 1 if flashback was obtained; otherwise, it was 0. Table 1 presents summarized information on the number of successful trials and attempts.

Table 1.

Number (and percentage) of cannulation attempts analyzed in the dataset

Number of Trials or Attempts	Whole Dataset (51 Participants)	Two Groups by GRS Score		Two Groups by ocScores
		11 HPGRS	12 LPGRS	11 HPOC	12 LPOC
num_trials	649	162	133	160	114
num_trials (num_atts>1)	47 (7%)	2 (1%)	21 (16%)	3 (2%)	22 (19%)
num_trials (success=1)	622 (96%)	162 (100%)	120 (90%)	160 (100%)	99 (87%)
num_atts	720	164	166	164	143
num_atts (success=1)	624 (87%)	163 (99%)	120 (72%)	160 (98%)	99 (69%)

Note that HPs and LPs are subsets of the whole dataset who were identified either by subjective expert ratings (GRS) or by objective ocScores. GRS, Global Rating Scale; ocScore, outcome score; HP, high performer; LP, low performer.

In addition, other previously defined flashback quality metrics were also used, including the number of infiltrations (num_infil), flash ratio (FR),¹³ and flash efficiency (FE).¹⁷ FR is a measure of a user's ability to maintain flashback, while FE is a measure of a user's ability to achieve and maintain flashback. When flashback did not occur, num_infil, FR, and FE were set to not applicable, 0, and 0, respectively. The distributions of the three outcome metrics are shown in Figure 2.

Figure 2.

Distributions of the three flashback quality outcome metrics (num_infil, FR, and FE) for attempts from 51 participants. FE, flash efficiency; FR, flash ratio; num_infil, number of infiltrations.

Performance Assessment Methodology

In this study, we analyzed needle insertions by cannulation attempts instead of by trials because each trial could comprise multiple cannulation attempts. This strategy enables a better understanding of the relationship between needle insertion angle and cannulation outcomes. There were a total of 816 cannulation trials from 51 participants. After removing trials with incomplete data because of hardware and/or software errors (e.g., battery failure) as well as those without flashback, 649 trials containing 720 attempts with flashback were retained.

Cannulation performance on the simulator was assessed in two ways: through subjective ratings (using GRS scores) and objective performance assessment metric (the previously developed ocScore¹⁷). The subjective way is expert observers' grading on cannulation performance during experiment via GRS checking list mentioned above. The total overall GRS scores from 51 participants ranged from three to seven. The cannulation outcome score (ocScore) is an objective metric that seeks to comprehensively assess cannulation outcome on the simulator. As defined previously,¹⁷ this composite metric combines the number of attempts, the num_infil, whether stable flashback is achieved, and the flashback efficiency of a trial. This score ranges from 0 to 1. For each subject, an ocScore is calculated per trial, and an average ocScore per participant can be calculated after the completion of all the subject's trials.

To answer research questions Q1 and Q2, we conducted logistic regression and Pearson correlation analysis to explore the relationship between angle and outcome metrics using processed data from all 51 participants. To answer Q3, we first defined two groups with distinct, separable skill levels as high performers (HPs) and low performers (LPs) from the 51 participants. From the expert-rated GRS sheets, we defined HP_GRS as those participants with overall scores of 7 and LP_GRS as those with overall scores of 3 or 4. As such, there were 11 HP_GRS and 12 LP_GRS. We also defined HPs and LPs on the basis of ocScore: Among the 51 participants, the top 11 ocScores were defined as HP_OC and those with the 12 lowest ocScores were defined as LP_OC. The mean of the average ocScore from HP_OC and LP_OC was 0.60 (SD: 0.03) and 0.19 (SD: 0.06), respectively. To examine whether needle angle metrics were different between the two groups, two-sample t tests or Mann–Whitney U tests were used, depending on the normality of the data. The level of significance was set to 0.05.

Results

Relationship between Angle Metrics and Cannulation Success

To examine whether cannulation success can be predicted by angle metrics, we constructed univariate logistic regression models to predict the probability of cannulation success (P(success=1)) for each of the angle metrics (${\overline{\alpha }}_S,\#{angle\_changes}_S,or\hspace{0.17em}{LDLJ\left(\alpha \right)}_S$) (see Figure 3 and Table 2). These models used data from whole cannulation attempts (S) from all 51 participants.

Figure 3.

Hill functions of statistically significant angle metrics related to successful cannulation from the univariate logistic regression models. $\overline{\alpha }$, average insertion angle; #angle_changes, number of times the needle angle is changed; LDLJ(α), log dimensionless jerk of insertion angle; P (success=1), probability of cannulation success.

Table 2.

The results of univariate logistic regression models between angle-based metrics and the probability of cannulation success

Logistic Regression Models	Coefficients (P Values)
	${\overline{\alpha }}_S$	#angle_changess	LDLJ(α)s
${P\left(success=1\right)=f(\overline{\alpha }}_S)$	−0.077 (<0.001)	—	—
P(success=1)=f(#angle_changess)	—	−0.276 (<0.001)	—
P(success=1)=f(LDLJ(α)s)	—	—	0.149 (<0.001)

$\overline{\alpha }$, average insertion angle; #angle_changes, number of times the needle angle is changed; LDLJ(α), log dimensionless jerk of insertion angle; P(success=1), probability of cannulation success.

From the P(success=1) models, we observed that ${\overline{\alpha }}_S$ and #angle_changes_s are negatively associated with P(success=1) (odds ratio=0.926, P value < 0.001; odds ratio=0.759, P value < 0.001, respectively). This suggests that as average needle angle increases or the amount of “digging” with the needle increases during cannulation, the probability of obtaining flashback decreases. Similarly, we observed a positive association between the probability of obtaining flashback and LDLJ(α)_s, the metric that quantifies smoothness of movement with the needle (odds ratio=1.161, P value < 0.001). From this, one can infer that as angle smoothness increases, the probability of obtaining flashback improves.

The Hill functions of the angle metrics related to P(success=1) from the univariate logistic regression models are shown in Figure 3. In accordance with Kidney Disease Outcomes Quality Initiative guidelines, the univariate model relating average needle angle to P(success=1) indicates that if the average needle angle (${\overline{\alpha }}_S$) falls in the 10°–30° range, there is a high likelihood of success (P(success=1)≥0.8). On the other hand, a consistently higher average needle angle (${\overline{\alpha }}_S$≥30°) results in a lower likelihood of cannulation success. Furthermore, as can be seen in the figure, as #angle_changes_s decreases, the likelihood of success increases. In general, a small number (≤7) of needle angle changes during cannulation result in a high likelihood of success (≥0.8).

Similarly, the univariate model predicting cannulation success from LDLJ(α)_s suggests that attempts with smooth needle motion during cannulation, as quantified by LDLJ(α)_s≥ −30, result in a greater likelihood of success (P(success=1)≥0.8).

Relationship between Angle Metrics and Flashback Quality

We also investigated the relationship between angle metrics and quality of blood flashback (num_infil, FR, and FE) on the simulator using correlation analysis on the basis of the whole dataset. The correlation coefficients of Pearson correlation analysis between the two sets of metrics are presented in Table 3.

Table 3.

Correlation coefficients (P values) between the angle metrics and flashback quality metrics from 51 participants

Angle Metrics on Three Segments		Flashback Quality Metrics
		num_infil	FR	FE
$\overline{\alpha }$	${\overline{\alpha }}_S$	0.054 (0.18)	−0.192 (<0.001)	−0.238 (<0.001)
	${\overline{\alpha }}_{s1}$	0.152 (<0.001)	−0.118 (0.003)	−0.004 (0.91)
	${\overline{\alpha }}_{s2}$	0.044 (0.27)	−0.090 (0.024)	−0.173 (<0.001)
The number of angle changes (#angle_changes)	#angle_changess	0.473 (<0.001)	−0.528 (<0.001)	−0.440 (<0.001)
	#angle_changess1	0.110 (0.006)	−0.185 (<0.001)	−0.389 (<0.001)
	#angle_changess2	0.589 (<0.001)	−0.556 (<0.001)	−0.102 (0.01)
LDLJ(α)	LDLJ(α)s	−0.447 (<0.001)	0.463 (<0.001)	0.371 (<0.001)
	LDLJ(α)s1	−0.095 (0.018)	0.186 (<0.001)	0.580 (<0.001)
	LDLJ(α)s2	−0.513 (<0.001)	0.463 (<0.001)	−0.065 (0.11)

$\overline{\alpha }$, average insertion angle; #angle_changes, number of times the needle angle is changed; LDLJ(α), log dimensionless jerk of insertion angle.

For the $\overline{\alpha }$, several metrics significantly correlated with flashback quality as presented in Table 3. As can be noted, however, the magnitude of correlation coefficients is generally low (between 0.090 and 0.238). For needle “digging” (#angle_changes), the angle metrics correlated more strongly with flashback quality metrics. Specifically, needle “digging” in both whole (S) and partial (s₁, s₂) cannulation segments positively correlated with the num_infil, with values ranging from 0.110 for before flashback to 0.589 for after flashback segments. In addition, both average angle and the number of angle changes correlated negatively with FR and FE, indicating higher values for these metrics result in poorer flashback quality.

Strong correlations were also observed between LDLJ(α) and flashback quality metrics. LDLJ(α)_s2, which quantifies angle “smoothness” from the time a user obtains flashback until the end of the attempt, was most strongly correlated with num_infil (−0.513). LDLJ(α)_s2 also demonstrated a significant correlation with FR (0.463) since FR measures the ability of a user to maintain flashback after flashback was first achieved. In the preflasback segment (s₁), LDLJ(α)_s1 was highly correlated with FE (0.580) since FE measures the ability to achieve flashback.

Distinguish Skill Levels Using Angle Metrics

To examine whether angle metrics can differentiate between skill levels, we performed statistical analysis (two-sample t tests and Mann–Whitney U tests) between the HP and LP groups (see Figure 4, Table 4). As defined earlier, HPs and LPs were defined in two ways: using subjective GRS ratings and objective ocScore values from the simulator (see Performance Assessment Methodology Section). From the 11 HPs and 12 LPs, 54.5% (6/11) of HPs were identified as such by both GRS and ocScore, while 50% (6/12) of LPs (LP_GRS and LP_OC) overlapped.

Table 4.

Statistical results (mean and 95% confidence interval) of angle-based metrics of 11 high performers and 12 low performers on three segments for all attempts using two-sample t tests or Mann–Whitney U tests

Angle Metrics on Three Segments		11 HPGRS versus 12 LPGRS		11 HPOC versus 12 LPOC
		Means	95% CI	Means	95% CI
$\overline{\alpha }$	${\overline{\alpha }}_S$	23.13 versus 26.41	(−5.33 to −1.91)b	21.05 versus 26.06	(−6.64 to −3.69)a
	${\overline{\alpha }}_{s1}$	25.86 versus 28.36	(−4.47 to −0.53)a	25.05 versus 27.28	(−4.30 to −0.69)b
	${\overline{\alpha }}_{s2}$	22.05 versus 23.65	(−3.33 to 0.13)a	19.91 versus 24.37	(−5.98 to −2.93)a
The number of angle changes (#angle_changes)	#angle_changess	3.49 versus 5.36	(−2.00 to −1.00)b	3.90 versus 5.42	(−2.00 to −1.00)b
	#angle_changess1	1.41 versus 2.81	(−2.00 to −1.00)b	1.63 versus 2.99	(−1.00 to −0.99)b
	#angle_changess2	2.40 versus 2.99	(−1.00 to 0.0002)b	2.83 versus 2.44	(0.0003 to 1.00)b
LDLJ(α)	LDLJ(α)s	−23.33 versus −24.44	(0.30 to 2.17)b	−23.81 versus −25.24	(0.33 to 2.33)b
	LDLJ(α)s1	−15.90 versus −18.35	(1.38 to 3.51)a	−15.39 versus −19.82	(2.98 to 5.13)b
	LDLJ(α)s2	−21.06 versus −20.60	(−1.94 to 0.40)b	−21.98 versus −19.30	(−3.93 to −1.69)b

HP, high performer; LP, low performer; CI, confidence interval; $\overline{\alpha }$, average insertion angle; #angle_changes, number of times the needle angle is changed; LDLJ(α), log dimensionless jerk of insertion angle.

^aTwo-sample t tests.

^bMann–Whitney U tests.

The $\overline{\alpha }$ for the whole attempt (S) as well as for the before flashback segment (s₁) was significantly different for HP and LP groups when classified via both GRS and ocScore methods. HPs had significantly lower average angles than LPs in the whole attempt (S) and preflashback (s₁) segments. In addition, when classifying HPs and LPs using ocScore, we observed significant differences in $\overline{\alpha }$ in the s₂ segment as well (after flashback).

The number of angle changes (#angle_changes) metric, a measure of “digging” with the needle during cannulation, was significantly lower for HPs in most of the cannulation segments; that is, HPs had fewer motion reversals during cannulation. This behavior was evidenced when whole attempts (S) were analyzed, as well as when attempts were segmented into before (s₁) and after (s₂) flashback segments (except for s₂ when classified by GRS). Similarly, smoothness of needle angle (LDLJ(α)) also demonstrated differences between HPs and LPs when categorized via GRS and ocScore (see Table 4). HPs had significantly less needle “digging” and smoother motion as quantified by lower #angle_changes and higher LDLJ(α), respectively, at both the whole attempt (S) and before flashback (s₁) cannulation segments. LDLJ(α) demonstrated statistical significance in the after flashback segment (s₂) only when classified by ocScore.

Discussion

Cannulation plays a critical role in maintaining a functional VA, which, in turn, decreases reliance on tunneled dialysis catheters for routine dialysis. Skilled cannulation is also critical for preventing access maturation failures¹⁹ and providing a positive patient experience. It is well known that cannulation for HD is problem ridden because of the complex patient-specific geometries and relatively large needle sizes.^1,19 As such, it is reported that >50% of cannulations are suboptimal, resulting in minor infiltrations, while 5%–7% result in major infiltrations that could cause a “cascade” of significant medical complications. Consequently, infiltrations could result in ultimately rendering the patient's VA unusable.²⁰ Therefore, one of the most critical factors in improving clinical outcomes related to VA is ensuring skilled cannulation in our dialysis clinics.

Traditionally, several published resources for nephrology nurses and PCTs have prescribed ranges of angles to be used for effective cannulation of AV fistulas and AV grafts.²¹ For instance, nephrology nurse Deborah Brouwer-Maier, who pioneered teaching cannulation via cannulation camps, prescribes a 20°–35° angle range for AV fistulas and a 45° angle for AV grafts.²¹ Although it is intuitive that the geometries of fistulas and needles affect needle angle, no study has quantitatively examined angle behavior during cannulation and how this affects cannulation outcomes. In this study, we used a novel cannulation simulator designed for the specific purpose of comprehensively assessing cannulation skill to examine the relationship of angle behavior to cannulation skill. Toward this goal, we have previously described how our methods quantitatively measure needle angle in real time throughout the process of cannulation on this simulator.¹⁸ In addition, we have also demonstrated that objective metrics on our simulator have significant advantages over subjective ratings of skill by experts.¹⁷ These simulator-based methods allow detailed and quantitative examination of the process of cannulation.

This study revealed that the average needle angle is decisively related to cannulation outcomes. That is, our model reveals a statistically significant relationship between the P(success=1) and average needle angle. Similarly, average needle angle is also related to the num_infil before flashback and the two flashback quality metrics. On the basis of these results, the intuition that needle angle matters for cannulation can now be substantiated via the analyses presented here. We have also demonstrated that needle angle behavior is different for skilled versus unskilled cannulators. Specifically, when participants were classified as skilled versus unskilled by either objective or subjective scores, angle metrics were demonstrably different. Such simulator-based skill assessment could potentially be useful in raising the bar in cannulation skill by establishing competency-based benchmarks. For instance, several medical specialties have now implemented competency-based curricula (often on simulators) with established benchmarks for demonstrating skill proficiency. Such an approach, if implemented for HD cannulation, could result in tangible improvements on patient outcomes.

In contrast to the prevailing assumption that cannulation experience is synonymous with expertise, we used two methods in this article as indicators for skill: a Likert scale rating sheet GRS and the objective ocScore metric. In a previous study, we demonstrated that ocScore is better suited for assessing cannulation skill than subjective ratings by preceptors.¹⁷ In line with the previous study, while both GRS and ocScore revealed skill differences in average angle between HPs and LPs, classification by ocScore yielded significant differences in more metrics and segments. When HPs and LPs were classified using ocScore, average angle differentiated between the skill levels in all three cannulation segments (S, s₁, s₂) as well as demonstrated greater separation between the two groups. This observation supports the results we have observed in previous studies with respect to the relationships between process metrics, ocScore, and the GRS score.^14,17

In addition to measuring needle angle, a novel contribution of this work is to formulate two metrics that measure more nuanced aspects of cannulation on the basis of the angle profiles. “Digging” movements made with the needle were measured using the number of angle changes metric, while the “smoothness” of motion with the needle was measured used LDLJ(α). Both of these metrics have important implications for cannulation success (e.g., avoiding infiltration) as well as for patient experience (e.g., minimizing pain).^7,22,23 Our results suggest that these metrics that measure finer aspects of cannulation are more closely linked with successful cannulation outcomes than average angle. For instance, needle “digging” significantly predicted the likelihood of cannulation success; as the #angle_changes_s was >7, the likelihood of cannulation success drastically dropped. Similarly, needle “smoothness” (measured by LDLJ(α)) also significantly predicted cannulation outcome and an LDLJ(α)_s< −30 resulted in drastically lowered probability of success.

This study also has several limitations. Although the simulator is a large step above commonly used tools for basic cannulation skills training (e.g., “fake” mannikin arms, turkey legs), this device has not been validated in clinical settings. That is, use of the simulator has not yet been demonstrated to improve clinical cannulation. However, the results from this study are a first step toward future clinical validation since the metrics and methods presented here will be used in these future studies. Furthermore, the current simulator fistula models do not include more difficult-to-access fistulas (e.g., segments with varying depths) or stenosed ones.

In conclusion, the methods and results presented in this work could lead to competency-based assessment and training for cannulation. In the future, we plan to conduct a clinical study examining the effect of training on the simulator on cannulation in clinical settings. We hope that large-scale implementation of validated simulator-based training methods on the basis of this work will translate to better patient outcomes and improved overall health care delivered to patients receiving HD.

Disclosures

J. Bible reports the following: Consultancy: Purac America; Wm. Keith Dozier. D. Brouwer-Maier reports the following: Employer: Transonic Systems Inc.; Consultancy: B Braun Medical Inc.; Advisory or Leadership Role: Medical Education Institute (MEI)—BOD; and Other Interests or Relationships: Member- ANNA, ASDIN, VASA; ANNA DEI Committee; MEI DOD. J. Geissler reports the following: Advisory or Leadership Role: Vascular Access Society of Americas-Board Member and Education committee—Volunteer positions travel reimbursement for conference only; and Speakers Bureau: Not Paid—Vascular Access Society of Americas—speaker as a committee member. P. Roy-Chaudhury reports the following: Consultancy: Akebia, Alexion (Astra-Zeneca Rare Diseases), Astra-Zeneca, Bayer, Becton Dickinson, Cormedix, Humacyte, Medtronic, WL Gore; Ownership Interest: Chief Scientific Officer and Founder, Inovasc LLC; Research Funding: NIH Small Business Grants as MPI or site PI with; Adgero, Cylerus, Eko, and Inovasc; Honoraria: Akebia, Alexion (Astra-Zeneca Rare Diseases), Astra-Zeneca, Bayer, Becton Dickinson, Cormedix, Humacyte, Medtronic, N9, WL Gore; and Advisory or Leadership Role: Akebia, Alexion (Astra-Zeneca Rare Diseases), ASN, Astra-Zeneca, Bayer, Becton Dickinson, BioMed Innovations, Cormedix, Humacyte, Medtronic, N9, Vascular Access Society of the America's, WL Gore; Editorial Board, Journal of Vascular Access. R. Singapogu reports the following: Ownership Interest: Radiant Ventures LLC; Sojourn MedTech LLC; and Patents or Royalties: Clemson University. All remaining authors have nothing to disclose.

Funding

This work was supported by research grant K01DK111767 from the National Institute of Diabetes and Digestive and Kidney Diseases (R. Singapogu).

Author Contributions

Conceptualization: Judy Geissler, Ravikiran Singapogu.

Formal analysis: Joe Bible, Prabir Roy-Chaudhury, Ravikiran Singapogu, Ziyang Zhang.

Funding acquisition: Prabir Roy-Chaudhury, Ravikiran Singapogu.

Investigation: Deborah Brouwer-Maier, Judy Geissler, Lydia Petersen, Prabir Roy-Chaudhury, Ziyang Zhang.

Methodology: Joe Bible, Deborah Brouwer-Maier, Judy Geissler, Ravikiran Singapogu, Ziyang Zhang.

Project administration: Ravikiran Singapogu.

Resources: Judy Geissler.

Software: Lydia Petersen, Ziyang Zhang.

Supervision: Ravikiran Singapogu.

Validation: Joe Bible, Judy Geissler, Lydia Petersen, Prabir Roy-Chaudhury.

Writing – original draft: Ziyang Zhang.

Writing – review & editing: Joe Bible, Deborah Brouwer-Maier, Judy Geissler, Lydia Petersen, Ravikiran Singapogu.

Supplementary Material

kidney360-4-0962-s001.pdf ^{(551.7KB, pdf)}

Supplemental Material

This article contains the following supplemental material online at http://links.lww.com/KN9/A353.

Supplemental Figure 1. An example of how to extract the sensor data of attempts in one cannulation trial.

Supplemental Figure 2. An illustration of the cannulation of the needle insertion angle.

Supplemental Table 1. The definition and the calculation of the metrics used in this paper.