Feature Articles

Operative Intervention Does Not Change Pain Perception in Patients With Diabetic Foot Ulcers

  1. Olivia V. Waldman1,*,
  2. Stephanie P. Hao1,*,
  3. Jeff R. Houck2,
  4. Nicolette J. Lee3,
  5. Judith F. Baumhauer1 and
  6. Irvin Oh1
  1. 1Department of Orthopaedics and Rehabilitation, University of Rochester Medical Center, Rochester, NY
  2. 2Department of Physical Therapy, George Fox University, Newburg, OR
  3. 3Sydney Kimmel Medical College at Thomas Jefferson University, Philadelphia, PA
  1. Corresponding author: Irvin Oh, irvinoh{at}gmail.com
Clinical Diabetes 2020 Apr; 38(2): 132-140. https://doi.org/10.2337/cd19-0031

Abstract

Researchers investigated pain perception in patients with diabetic foot ulcers (DFUs) by analyzing pre- and postoperative physical function (PF), pain interference (PI), and depression domains of the Patient-Reported Outcome Measurement Information System (PROMIS). They hypothesized that 1) because of painful diabetic peripheral neuropathy (DPN), a majority of patients with DFUs would have high PROMIS PI scores unchanged by operative intervention, and 2) the initially assessed PI, PF, and depression levels would be correlated with final outcomes. Seventy-five percent of patients with DFUs reported pain, most likely because of painful DPN. Those who reported high PI and low PF were likely to report depression. PF, PI, and depression levels were unchanged after operative intervention or healing of DFUs.

Diabetic foot ulcers (DFUs) are a debilitating and common side effect of diabetes, experienced by up to 25% of people with diabetes (1–3). Foot ulceration is caused by a combination of internal and external risk factors that lead to the breakdown of skin, resulting in an exposed wound that can quickly become infected (2). Repetitive trauma or pressure in an insensate foot due to diabetic peripheral neuropathy (DPN) is the most common cause of DFUs, which can present with numbness, pain, or weakness (1,2). Some patients with DFUs report foot numbness without pain, whereas some report a significant amount of pain that affects clinical care. An estimated 10–20% of all people with diabetes experience painful DPN, which can occur with or without the presence of a DFU and at any stage in the disease (4–6). In such cases, DPN pain can result in lower reported quality of life and carries a doubled risk of depression in patients with diabetes (1,4,7,8).

Because of varying symptoms and extent of infection, understanding the nature of a DFU patient’s pain can be challenging for clinicians. Although many patients with DFUs expect decreased pain after successful treatment and healing of their ulcer, persistent pain after healing is prevalent (9).

Meanwhile, nontherapeutic opioid use has become a public health crisis, especially in the United States, where 80% of the global opioid supply is consumed (10). The opioid abuse epidemic that has enveloped the United States has not been slowing with awareness, but rather has increased threefold in recent years (10).

The Patient-Reported Outcomes Measurement Information System (PROMIS) is a patient health measurement instrument that has been funded by the National Institutes of Health since 2004. Studies have shown that PROMIS accurately reports changes in patient-reported conditions (11,12). Among various domains of PROMIS, the physical function (PF), pain interference (PI), and depression scales have been widely used in medicine (13). PROMIS has been identified as a more reliable measurement than the Numeric Pain Rating Scale (NPRS), the Foot and Ankle Ability Measure, and the Foot Function in Index in assessing foot and ankle pain and can benefit patient care, research, and communication (14,15). The PROMIS PI scale especially has been demonstrated to be superior to the NPRS in assessing pain levels in foot and ankle patients (14). PROMIS uses item response theory and computerized adaptive testing (CAT) to yield a high level of precision and specificity with the fewest responses required from patients (11,16,17). The questions are designed to be readable by individuals of various education levels to promote accuracy in a diverse patient population (18). Despite the widespread use of PROMIS, there has been no report of this tool being used to assess pain experienced by patients with DFUs. Accurate understanding of the nature of diabetic foot pain and how it changes with operative intervention for DFUs will help clinicians effectively manage, educate, and set expectations of patients regarding perioperative pain.

This study aimed to assess changes in pain intensity associated with operative intervention in DFU patients using the PROMIS PI domain and to investigate clinical factors that may influence pain perception. We hypothesized that 1) patients with DFUs would experience higher levels of PI and depression and lower PF than the average U.S. population; 2) PI, PF, and depression scores would not change significantly after operative intervention; and 3) PI, PF, and depression would be correlated. In particular, we hypothesized that the combination of worse PF and PI compared with the general U.S. population would be correlated to more severe depression.

Research Design and Methods

Participants

Data collection was conducted in a single academic orthopedic surgeon’s practice from February 2015 to November 2018. A total of 240 patients who underwent operative intervention of an infected DFU during this time period were identified using code E11.621 from the International Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10). Patients were excluded from the study if they had fewer than three PROMIS assessments or incomplete PROMIS data, if their postoperative follow-up was <3 months, or they had recurrent infections within 3 months. Ninety-two patients met the inclusion criteria for this study, and their data were used to quantify PROMIS domain changes and make minimal clinically important difference (MCID) calculations from their initial visits to their final follow-up visits (Figure 1). An additional 19 patients were followed up for <3 months but met all other inclusion criteria. Patient demographics were calculated and χ2 analysis of scores from the initial visits was performed using patient data from the combined total (n = 111).

FIGURE 1
FIGURE 1

Patient selection flowchart.

PROMIS assessments considered complete included PROMIS CAT PI (version 1.1), PROMIS CAT PF (version 2.0), and PROMIS CAT depression (version 1.0) scales (19–21). They were completed by patients in the clinic’s waiting area before scheduled visits. Demographic data, BMI, A1C, presence of chronic renal failure (CRF), type of amputation, and wound healing information were collected during follow-up appointments. Thorough chart reviews and physical exams, such as the 5.07 (10G) monofilament test, were conducted to diagnose or document the presence of DPN (1,3,6).

Statistical Analyses

Descriptive statistical analyses were conducted for patient factors, clinical variables, and PROMIS scores (PF, PI, and depression scales).

Severity of symptoms defined by the PROMIS scales were determined by calculating the proportion of patients in standard deviation (SD) increments above and below the U.S. averages (T-score 50) (hypothesis 1). The categories included at or better than (>0) the U.S. average (score = 0), 1 SD worse (0 to –9.99) than the U.S. average (score = –1), 2 SDs worse (–10 to –19.99) than the U.S. average (score = –2), 3 SDs worse (–20 to –29.99) than the U.S. average (score = –3), and >3 SDs worse (−30 or lower) than the U.S. average (score = –4). This calculation resulted in each PROMIS scale being converted to an ordinal scale varying from 0 to –4 to assess symptom severity.

To determine the association of clinical variables with symptom severity, clinical variables were coded as follows: glycemic control: A1C >7% = 1, A1C ≥7% = 0; renal function: CRF present = 1, CRF absent = 0; type of surgical intervention: irrigation and debridement = 0, forefoot (toe or ray) amputation = 1, mid/hind foot (Lisfranc, chopart, or calcanectomy) amputation = 2, Syme or above amputation = 3; and ulcer healing status: healed = 1, not healed = 0. Each of these clinical variables was assessed for association with symptom severity using χ2 analysis.

Changes in clinical variables and PROMIS scores were evaluated using T-scores to calculate the proportions of patients achieving and not achieving an MCID. After evaluating normality (all PROMIS scales skewness and kurtosis were <2.0), changes in PROMIS scores (PF, PI, and depression) from the initial visit to the longest follow-up visit were calculated using paired t tests to test our second hypothesis. To further evaluate change in symptoms from the initial visit to longest follow-up, the proportions of patients with improvement in MCID, deterioration in MCID, and change in MCID were calculated for each PROMIS scale. Improvement in MCID is well discussed in PF and PI studies (22,23). Values for MCID improvement range from 3 to >5 T-score points (22,23). This study used 4 T-score points to determine an MCID improvement or deterioration, recognizing that future studies may further modify estimates of deterioration or improvement depending on their choice of MCID.

To assess the co-occurrence of symptoms defined by the PROMIS scales, Spearman correlations and χ2 analyses were used. Spearman ρ statistics were used to calculate the correlation among PROMIS T-score values at the initial visit and among PROMIS score changes from the initial visit to the longest follow-up. Similar to prior studies using the PROMIS (14), correlation strengths were categorized as follows: strong (≥0.7), strong-moderate (0.61–0.69), moderate (0.4–0.6), moderate-poor (0.31–0.39), or poor (≤0.3).

To determine the association of symptom severity, 2 × 2 tables were analyzed using χ2 statistics from the initial PROMIS assessments and for MCID categories of change.

To assess the combined influence of PROMIS PF and PI on depression, a composite score was used similar to a recent study (24). PROMIS PF and PI were summed to create a composite variable; the summed PF and PI score from initial PROMIS assessments ranged from –8 to 0. The summed PF and PI MCID categories ranged from –2 to 2. Examining the summed PF and PI from the initial PROMIS assessments and change scores allowed the evaluation of the combined severity of symptoms associated with depression (hypothesis #3). All analyses were done with SPSS version 25.0 software.

Results

Patient Characteristics

At the initial visit, 111 participants had data available, including 92 participants with a minimum of 3 months of postoperative follow-up (Table 1). For both samples, the majority were male (79.3 and 80.4%, respectively) with a mean age of 62.2 and 60.5 years and a mean BMI of 33.8 and 34.1 kg/m2, respectively. The mean follow-up duration was 4.7 months (range 3–12) (Table 1). Operative procedures performed included irrigation and debridement (n = 39), forefoot amputations (n = 46), mid/hindfoot amputations (n = 14), and Syme or above amputations (n = 12). The average initial Wagner score was 2.92 (range 1.0–4.0). Sixty-three patients (68.5%) had healed DFUs by the longest follow-up. The average initial A1C was 8.1% (range 4.8–13.6), and the average final A1C was 7.8% (range 4.8–13.1). Twenty-two patients (23.9%) were diagnosed with CRF.

View this table:
TABLE 1

Patient Demographics

PROMIS Scores

The mean initial PROMIS PF, PI, and depression scores were 34.4 (range 19.1–53.1), 58.7 (range 38.7–76.5), and 51.4 (range 34.2–78.9), respectively. The majority of patients (57.6 and 76.5%, respectively) reported PI and PF at least 2 SDs worse than the U.S. average (Figure 2). Clinical variables that showed an association with PF symptom severity included amputation type (χ2 42.1, P <0.01) and CRF (χ2 9.7, P = 0.05). PI symptom severity was associated with ulcer healing status (χ2 12.2, P <0.01). No other clinical variables were significantly associated with PI, PF, or depression symptom severity.

FIGURE 2
FIGURE 2

Symptom severity by 1-SD increments across PROMIS scales. Mean initial PROMIS PF, PI, and depression scores assessed by SDs below the U.S. population average. High raw PI T-scores were transformed into SDs lower than those of the U.S. population.

At the final follow-up, the mean PI score decreased by 0.1 (range –26.6 to 29.5), the mean PF score increased by 1.7 (range –23.2 to 25.4), and the mean depression score increased by 0.2 (range –25.2 to 32.7) (Table 1). Most patients did not achieve MCID in PF, PI, or depression from the initial visit to the longest follow-up visit (47.8, 40.2, and 46.7% remained the same, respectively). An improved MCID in PF, PI, or depression was noted in 33.7, 34.8, and 27.2% of patients, respectively. A decline equal to or greater than an MCID in PF, PI, or depression was noted in 18.5, 25.0 and 26.1% of patients, respectively (Figure 3).

FIGURE 3
FIGURE 3

Documenting change in patients with DFUs. Patients are categorized by MCID improvement or decline from their initial appointment to their longest follow-up visit.

Statistically significant moderate correlations among scales existed for PROMIS T-score values and symptom severity at the initial visit. The strongest correlation was noted between PI and depression (P <0.01, Spearman ρ = 0.44). χ2 Analysis of PI and depression symptom severity showed a significant association (Table 2). The 2 × 2 table revealed that patients who reported a PI >60 were more likely to report depression. There was also a moderate to poor correlation between PF and depression (P <0.01, ρ = 0.39). χ2 Analysis of PF and depression symptom severity also showed a significant association (Table 2). The 2 × 2 table showed that patients who reported a low PF were also more likely to report a depression score >50 (Table 2). The Spearman correlation between PF and PI was moderate to poor (P <0.01, ρ = –0.27), and the χ2 analysis PI and PF symptom severity was also significant. At the initial time point, as the MCID sum of PF and PI increased, depression scores >50 decreased (Table 3).

View this table:
TABLE 2

χ2 Analysis of PI and Depression at Initial Time Point (P = 0.001, Spearman ρ = 0.49)

View this table:
TABLE 3

χ2 Analysis of PF and Depression at Initial Time Point (P = 0.007, Spearman ρ = 0.41)

There were also significant poor to moderate correlations among changes in PROMIS scales and among MCID change in PROMIS scales. The strongest correlation was between change in PI and change in depression (P <0.01, ρ = 0.47). χ2 Analysis of MCID categories for PI and depression also showed a significant association (P <0.01). There was also a significantly poor correlation between MCID categories for PF and PI (P <0.01, ρ = –0.28). χ2 Analysis of PF and depression symptom severity also showed a significant association (Table 2). There was no significant correlation between MCID categories for PF and depression (P = 0.15, ρ = –0.15), and χ2 analysis of MCID categories for PF and depression showed significant association (P = 0.04). The correlation of the sum of the change in PF and PI scores was significant (P = 0.02, ρ = 0.24), and χ2 analysis showed a significant association (P <0.01) of MCID categories of depression with the sum of categories of MCID change for PF and PI (Table 4).

View this table:
TABLE 4

χ2 Analysis of MCID PF and PI Sum Versus MCID for Depression

Discussion

Despite having reduced sensation, many DFU patients experience significant pain. Our results indicate that the pain perception of DFU patients is unlikely to change despite successful operative intervention. PROMIS assessments demonstrate the potential to improve care by allowing for better communication between patients and providers, in addition to more reliable pain assessment and valid data collection (11). The predictive nature of PROMIS data are especially helpful in improving operative decisions and patient care (25). This is the first study to use PROMIS scores in an attempt to characterize the pain experienced by DFU patients.

Our data showed that 75% of patients with infected DFUs report significant amounts of pain at initial presentation. The average PI score of our cohort was 58.9, with most patients reporting ∼2 SDs above the U.S. average (Figure 2). Ulcer healing status was the only clinical factor that was significantly related to PI (χ2 12.2, P <0.01). Other studies have found a similar percentage of patients with diabetes (40–60%) reporting pain (4,6).

Surprisingly, the average reported depression score (51.4) within the DFU population was not statistically significantly higher than in the general U.S. population (Table 1). Based on previous studies, we expected depression scores to be significantly higher among people with diabetes because of the bidirectional relationship between type 2 diabetes and depression (26). No clinical factors were significantly correlated with depression.

Reported PF was primarily 1.5 SDs below that in the average U.S. population (Figure 2). This was expected because of physical limitations caused by DFUs and resultant amputations (27–29). Amputation type (χ2 42.1, P <0.01) and CRF (χ2 9.7, P = 0.05) were the only clinical factors shown to affect PF.

From the initial visit to longest follow-up visit, mean changes in PI, PF, and depression were 1.7, –0.1, and 0.2, respectively (Table 1). Around one-third of our cohort showed an MCID improvement in PF, PI, or depression after operative intervention. The majority of our patients reported no change in PROMIS domains or sometimes worse outcomes after treatment (Figure 3). This finding indicates that operative intervention most likely did not change pain perception in the cohort.

Elevated depression and PI were commonly reported together (P <0.01, Spearman ρ = 0.44) at the initial time point in our study. Those who reported PI >60 were more likely to report depression >50 (Table 2). This observation is consistent with existing literature that also observed the coexistence of patient-reported pain and depression levels (4,30). Low PF and high PI scores were also found to be coexisting (P <0.01, ρ = –0.27) at the initial time point. DFU patients significantly reported both depression and low PF (P <0.01, ρ = 0.39) at the initial time point, and those who reported PF <40 were more likely to report depression (Table 2). Summing initial PF and PI MCIDs revealed that patients who simultaneously reported low PF and PI were more likely to report depression (Table 3). This finding held true for MCID assessment as well (Table 4).

MCID changes were statistically significant between depression and PI (P <0.01) as well as PF and PI (P = 0.02). On the other hand, MCID change for PF and depression was not statistically significant (P = 0.08). Other clinical factors tested for MCID were initial A1C, final A1C, CRF, type of operative intervention (irrigation and debridement, forefoot amputation, mid/hind foot amputation, and Syme or above amputation), wound-healing status, and length of follow-up (Table 1). No other clinical factors were found to be statistically significant for an MCID change (P >0.10).

Several methods are used for diagnosing DPN, with the Semmes-Weinstein 5.07 (10G) monofilament test being the most prevalent among health care providers because of its convenience and availability. However, monofilament tests lack the standardization needed to confirm the range of nerve fiber damage, which can vary among individuals (31). Differences in test application, interpretation, and populations all contribute to the low specificity and sensitivity of the monofilament test (2). Ultimately, there is no universal standard for diagnosing neuropathic pain. It has been suggested that more than one test of neurological deficits is required for the diagnosis of DPN; recommendations in the literature include using a combination of both clinical and diagnostic testing (32,33). Screening tests such as the Neuropathy Symptom and Change Score, Neuropathy Impairment Score, and Michigan Neuropathy Screening Instrument help with accurate and reliable diagnosis of DPN (34).

In our cohort of 111 patients, 48 were diagnosed with DPN by the senior author, who performed the Semmes-Weinstein 5.07 (10G) monofilament test to diagnose DPN. Fifty-five of our patients were previously diagnosed with DPN by another physician (primary care provider, neurologist, or endocrinologist). However, because of the lack of diagnostic standardization and documentation, we were not able to clearly confirm the validity of DPN in these patients. The remaining eight patients were not formally diagnosed with DPN by the senior author or another physician, although they were documented to have reduced sensation at their appointments. Even though there were generally higher levels of reported PI, the patient-reported PROMIS data do not conclusively point to painful DPN as the origin of the pain. Active infections also may have played a role in aggravation of DPN. For further research, using a patient-reported PROMIS measure of neuropathic pain may provide additional information to determine whether experienced pain is related to painful DPN or a separate comorbidity (35).

Studies have shown clinical depression to be present in 25% of patients with type 2 diabetes, and, as previously noted, depression risk has been shown to double in populations with diabetes (7,26). However, a previous study also found that DFUs and depressive symptoms are not significantly related (33). The authors suggest that patients with a depressed affect shared two major variables: a concern for the unpredictability and lack of treatment for their condition and a change in social self-perception that is caused by a decrease in PF (33). Because most of our cohort had low PF scores, a possible explanation for the lack of depression might be related to the first variable. If DFU patients understood their condition and treatment options, they might experience less depression and anxiety about the outcome. Screening study participants with the Patient Health Questionnaire-9 or a social determinants of health questionnaire may better assess the source of patient-reported depression (36–38).

In terms of postoperative care, our results indicate that DFU patients’ pain does not decrease postoperatively, suggesting that an opioid prescription is unlikely to benefit patients with DPN pain. Neuromodulating drugs such as amitriptyline, duloxetine, pregabalin, or gabapentin may be considered as initial treatment (39). Additionally, the DFU patient population appears to be particularly at risk for opioid abuse based on shared risk factors such as preoperative pain, depression, and low socioeconomic status (40). Studies have shown that benzodiazepine and antidepressant usage preoperatively puts patients at greater risk for opioid abuse postoperatively (40,41). Not only is there a chance of opioid abuse, but higher opioid dosages are also associated with increased complications, less satisfaction, and a greater pain intensity postoperatively (42,43).

This study is limited by its small sample size and lack of clinical variables assessed in the cohort. Despite initially recruiting 240 participants, only 111 were included in initial patient characteristic analyses, and 92 were included in further analyses of changes in PROMIS domains. The 111 patients met all inclusion criteria except for the criterion of 3-month postoperative follow-up. All analyses that identified change from the initial visit to the longest follow-up visit were conducted with the 92 participants who met all inclusion criteria and had at a minimum of 3 months of postoperative follow-up.

Demographic data such as race, ethnicity, and socioeconomic status were not included in the data analyses and could confound study results (2). Additionally, acknowledging that DFU patients often have multiple comorbidities, only a small number of possible diagnoses were used in analyses.

We chose a short follow-up duration to limit the confounding variables associated with multiple interventions. The longer the follow-up period, the more patients returned with recurrent DFUs at the same or different locations that required further operative interventions. To isolate the clinical analysis to a single operative event, we limited our follow-up to a 3- to 12-month period. Those who required an additional procedure within the time frame were excluded from the study.

Our average healing rate (68.5%) falls in the middle of those found in other studies, which have ranged from 33 to >80%, but studies have also shown that the average healing time is >2 months or, for uncomplicated cases, within 3 months (1,3). Although we found that depression in the DFU population was only slightly higher than in the general U.S. population (score of 51.4), this finding could be confounded by a short follow-up period. Studies have shown that patients with complications have higher depression rates (7). Therefore, if the follow-up time frame had been longer in this study, higher depression levels could have been observed as more complications arose.

Conclusion

Our results show that PF, PI, and depression rates are likely to remain the same even after the successful healing of a DFU. In addition, PROMIS symptoms tended to occur together, suggesting that providers should consider multidimensional assessment when attempting to address patients’ needs. Continuing to investigate this challenging patient population will allow for improvement in the physical and psychological functioning of patients with DFUs.

Article Information

Funding

This work was supported by a grant from the National Institutes of Health, National Institute of Arthritis and Musculoskeletal and Skin Diseases (R21AR074571).

Duality of Interest

The salaries for O.V.W. and S.P.H. were funded by the National Institutes of Health. No other potential conflicts of interest relevant to this article were reported.

Author Contributions

O.V.W. and S.P.H. contributed to the study design, data analysis, and manuscript writing. J.R.H. and N.J.L. contributed to data analysis and manuscript writing. J.F.B. contributed to the study design and manuscript writing. I.O. contributed to the study design, patient recruitment, data analysis, and manuscript writing. I.O. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Footnotes

  • ↵* O.V.W. and S.P.H. contributed equally as primary authors of this article.

https://www.diabetesjournals.org/content/license

Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/content/license.

References

  1. 1.↵
    1. Alavi A,
    2. Sibbald RG,
    3. Mayer D, et al
    . Diabetic foot ulcers: Part I. Pathophysiology and prevention. J Am Acad Dermatol 2014;70:1.e1–1.e18; quiz 19–20
    OpenUrl
  2. 2.↵
    1. Boulton AJM
    . The diabetic foot: from art to science. The 18th Camillo Golgi lecture. Diabetologia 2004;47:1343–1353
    OpenUrlPubMedWeb of Science
  3. 3.↵
    1. Prompers L,
    2. Huijberts M,
    3. Apelqvist J, et al
    . High prevalence of ischaemia, infection and serious comorbidity in patients with diabetic foot disease in Europe: baseline results from the Eurodiale study. Diabetologia 2007;50:18–25
    OpenUrlCrossRefPubMedWeb of Science
  4. 4.↵
    1. Davies M,
    2. Brophy S,
    3. Williams R,
    4. Taylor A
    . The prevalence, severity, and impact of painful diabetic peripheral neuropathy in type 2 diabetes. Diabetes Care 2006;29:1518–1522
    OpenUrlAbstract/FREE Full Text
  5. 5.
    1. Veves A,
    2. Manes C,
    3. Murray HJ,
    4. Young MJ,
    5. Boulton AJ
    . Painful neuropathy and foot ulceration in diabetic patients. Diabetes Care 1993;16:1187–1189
    OpenUrlAbstract/FREE Full Text
  6. 6.↵
    1. Callaghan BC,
    2. Cheng HT,
    3. Stables CL,
    4. Smith AL,
    5. Feldman EL
    . Diabetic neuropathy: clinical manifestations and current treatments. Lancet Neurol 2012;11:521–534
    OpenUrlCrossRefPubMedWeb of Science
  7. 7.↵
    1. Mansour AA,
    2. Jabir MA
    . The prevalence of comorbid depression among adults with diabetes in Southern Iraq. Pak J Med Sci 2007;23:185–187
    OpenUrl
  8. 8.↵
    1. Bair MJ,
    2. Robinson RL,
    3. Katon W,
    4. Kroenke K
    . Depression and pain comorbidity: a literature review. Arch Intern Med 2003;163:2433–2445
    OpenUrlCrossRefPubMedWeb of Science
  9. 9.↵
    1. Ribu L,
    2. Rustøen T,
    3. Birkeland K,
    4. Hanestad BR,
    5. Paul SM,
    6. Miaskowski C
    . The prevalence and occurrence of diabetic foot ulcer pain and its impact on health-related quality of life. J Pain 2006;7:290–299
    OpenUrlCrossRefPubMedWeb of Science
  10. 10.↵
    1. Morris BJ,
    2. Mir HR
    . The opioid epidemic: impact on orthopaedic surgery. J Am Acad Orthop Surg 2015;23:267–271
    OpenUrlCrossRefPubMed
  11. 11.↵
    1. Kitaoka HB,
    2. Meeker JE,
    3. Phisitkul P,
    4. Adams SB Jr.,
    5. Kaplan JR,
    6. Wagner E
    . AOFAS position statement regarding patient-reported outcome measures. Foot Ankle Int 2018;39:1389–1393
    OpenUrl
  12. 12.↵
    1. Hung M,
    2. Baumhauer JF,
    3. Licari FW,
    4. Bounsanga J,
    5. Voss MW,
    6. Saltzman CL
    . Responsiveness of the PROMIS and FAAM instruments in foot and ankle orthopedic population. Foot Ankle Int 2019;40:56–64
    OpenUrl
  13. 13.↵
    1. Anderson MR,
    2. Baumhauer JF,
    3. DiGiovanni BF, et al
    . Determining success or failure after foot and ankle surgery using Patient Acceptable Symptom State (PASS) and Patient Reported Outcome Information System (PROMIS). Foot Ankle Int 2018;39:894–902
    OpenUrl
  14. 14.↵
    1. Bernstein DN,
    2. Kelly M,
    3. Houck JR, et al
    . PROMIS Pain Interference is superior vs Numeric Pain Rating Scale for pain assessment in foot and ankle patients. Foot Ankle Int 2019;40:139–144
    OpenUrl
  15. 15.↵
    1. Nixon DC,
    2. McCormick JJ,
    3. Johnson JE,
    4. Klein SE
    . PROMIS Pain Interference and Physical Function scores correlate with the Foot and Ankle Ability Measure (FAAM) in patients with hallux valgus. Clin Orthop Relat Res 2017;475:2775–2780
    OpenUrl
  16. 16.↵
    1. Hung M,
    2. Baumhauer JF,
    3. Brodsky JW, et al.; Orthopaedic Foot & Ankle Outcomes Research (OFAR) of the American Orthopaedic Foot & Ankle Society (AOFAS)
    . Psychometric comparison of the PROMIS Physical Function CAT with the FAAM and FFI for measuring patient-reported outcomes. Foot Ankle Int 2014;35:592–599
    OpenUrlCrossRefPubMed
  17. 17.↵
    1. Riley WT,
    2. Pilkonis P,
    3. Cella D
    . Application of the National Institutes of Health Patient-reported Outcome Measurement Information System (PROMIS) to mental health research. J Ment Health Policy Econ 2011;14:201–208
    OpenUrlPubMed
  18. 18.↵
    1. Perez JL,
    2. Mosher ZA,
    3. Watson SL, et al
    . Readability of orthopaedic patient-reported outcome measures: is there a fundamental failure to communicate? Clin Orthop Relat Res 2017;475:1936–1947
    OpenUrl
  19. 19.↵
    1. Amtmann D,
    2. Cook KF,
    3. Jensen MP, et al
    . Development of a PROMIS item bank to measure pain interference. Pain 2010;150:173–182
    OpenUrlCrossRefPubMedWeb of Science
  20. 20.
    1. Rose M,
    2. Bjorner JB,
    3. Gandek B,
    4. Bruce B,
    5. Fries JF,
    6. Ware JE Jr.
    . The PROMIS Physical Function item bank was calibrated to a standardized metric and shown to improve measurement efficiency. J Clin Epidemiol 2014;67:516–526
    OpenUrlCrossRefPubMed
  21. 21.↵
    1. Lai J-S,
    2. Cella D,
    3. Choi S, et al
    . How item banks and their application can influence measurement practice in rehabilitation medicine: a PROMIS Fatigue item bank example. Arch Phys Med Rehabil 2011;92(Suppl.):S20–S27
    OpenUrlCrossRefPubMed
  22. 22.↵
    1. Hung M,
    2. Baumhauer JF,
    3. Licari FW,
    4. Voss MW,
    5. Bounsanga J,
    6. Saltzman CL
    . PROMIS and FAAM minimal clinically important differences in foot and ankle orthopedics. Foot Ankle Int 2019;40:65–73
    OpenUrl
  23. 23.↵
    1. Kenney RJ,
    2. Houck J,
    3. Giordano BD,
    4. Baumhauer JF,
    5. Herbert M,
    6. Maloney MD
    . Do Patient Reported Outcome Measurement Information System (PROMIS) scales demonstrate responsiveness as well as disease-specific scales in patients undergoing knee arthroscopy? Am J Sports Med 2019;47:1396–1403
    OpenUrl
  24. 24.↵
    1. Davis LL,
    2. Kroenke K,
    3. Monahan P,
    4. Kean J,
    5. Stump TE
    . The SPADE symptom cluster in primary care patients with chronic pain. Clin J Pain 2016;32:388–393
    OpenUrl
  25. 25.↵
    1. Ho B,
    2. Houck JR,
    3. Flemister AS, et al
    . Preoperative PROMIS scores predict postoperative success in foot and ankle patients. Foot Ankle Int 2016;37:911–918
    OpenUrlCrossRefPubMed
  26. 26.↵
    1. Semenkovich K,
    2. Brown ME,
    3. Svrakic DM,
    4. Lustman PJ
    . Depression in type 2 diabetes mellitus: prevalence, impact, and treatment. Drugs 2015;75:577–587
    OpenUrlCrossRefPubMed
  27. 27.↵
    1. Sinclair AJ,
    2. Conroy SP,
    3. Bayer AJ
    . Impact of diabetes on physical function in older people. Diabetes Care 2008;31:233–235
    OpenUrlAbstract/FREE Full Text
  28. 28.
    1. Meijer JW,
    2. Trip J,
    3. Jaegers SM, et al
    . Quality of life in patients with diabetic foot ulcers. Disabil Rehabil 2001;23:336–340
    OpenUrlCrossRefPubMedWeb of Science
  29. 29.↵
    1. Ribu L,
    2. Hanestad BR,
    3. Moum T,
    4. Birkeland K,
    5. Rustoen T
    . A comparison of the health-related quality of life in patients with diabetic foot ulcers, with a diabetes group and a nondiabetes group from the general population. Qual Life Res 2007;16:179–189
    OpenUrlCrossRefPubMedWeb of Science
  30. 30.↵
    1. Arnow BA,
    2. Hunkeler EM,
    3. Blasey CM, et al
    . Comorbid depression, chronic pain, and disability in primary care. Psychosom Med 2006;68:262–268
    OpenUrlAbstract/FREE Full Text
  31. 31.↵
    1. Wang F,
    2. Zhang J,
    3. Yu J, et al
    . Diagnostic accuracy of monofilament tests for detecting diabetic peripheral neuropathy: a systematic review and meta-analysis. J Diabetes Res 2017;2017:8787261
    OpenUrl
  32. 32.↵
    1. La Cesa S,
    2. Tamburin S,
    3. Tugnoli V, et al
    . How to diagnose neuropathic pain? The contribution from clinical examination, pain questionnaires and diagnostic tests. Neurol Sci 2015;36:2169–2175
    OpenUrl
  33. 33.↵
    1. Vileikyte L,
    2. Leventhal H,
    3. Gonzalez JS, et al
    . Diabetic peripheral neuropathy and depressive symptoms: the association revisited. Diabetes Care 2005;28:2378–2383
    OpenUrlAbstract/FREE Full Text
  34. 34.↵
    1. Xiong Q,
    2. Lu B,
    3. Ye H,
    4. Wu X,
    5. Zhang T,
    6. Li Y
    . The diagnostic value of Neuropathy Symptom and Change score, Neuropathy Impairment score and Michigan Neuropathy Screening Instrument for diabetic peripheral neuropathy. Eur Neurol 2015;74:323–327
    OpenUrl
  35. 35.↵
    1. Askew RL,
    2. Cook KF,
    3. Keefe FJ, et al
    . A PROMIS measure of neuropathic pain quality. Value Health 2016;19:623–630
    OpenUrl
  36. 36.↵
    1. Allen J,
    2. Balfour R,
    3. Bell R,
    4. Marmot M
    . Social determinants of mental health. Int Rev Psychiatry 2014;26:392–407
    OpenUrlCrossRefPubMed
  37. 37.
    1. Martin A,
    2. Rief W,
    3. Klaiberg A,
    4. Braehler E
    . Validity of the brief Patient Health Questionnaire mood scale (PHQ-9) in the general population. Gen Hosp Psychiatry 2006;28:71–77
    OpenUrlCrossRefPubMedWeb of Science
  38. 38.↵
    1. Kroenke K,
    2. Spitzer RL,
    3. Williams JBW
    . The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med 2001;16:606–613
    OpenUrlCrossRefPubMedWeb of Science
  39. 39.↵
    1. Patel KV,
    2. Allen R,
    3. Burke L, et al
    . Evaluation of composite responder outcomes of pain intensity and physical function in neuropathic pain clinical trials: an ACTTION individual patient data analysis. Pain 2018;159:2245–2254
    OpenUrl
  40. 40.↵
    1. Hah JM,
    2. Bateman BT,
    3. Ratliff J,
    4. Curtin C,
    5. Sun E
    . Chronic opioid use after surgery: implications for perioperative management in the face of the opioid epidemic. Anesth Analg 2017;125:1733–1740
    OpenUrl
  41. 41.↵
    1. Sun EC,
    2. Darnall BD,
    3. Baker LC,
    4. Mackey S
    . Incidence of and risk factors for chronic opioid use among opioid-naive patients in the postoperative period. JAMA Intern Med 2016;176:1286–1293
    OpenUrl
  42. 42.↵
    1. Cozowicz C,
    2. Olson A,
    3. Poeran J, et al
    . Opioid prescription levels and postoperative outcomes in orthopedic surgery. Pain 2017;158:2422–2430
    OpenUrl
  43. 43.↵
    1. Nota SPFT,
    2. Spit SA,
    3. Voskuyl T,
    4. Bot AGJ,
    5. Hageman MGJS,
    6. Ring D
    . Opioid use, satisfaction, and pain intensity after orthopedic surgery. Psychosomatics 2015;56:479–485
    OpenUrlCrossRefPubMed
View Abstract
Back to top
View Selected Citations (0)
Print
Article Alerts
Email Article
Citation Tools
Add to Selected Citations

Operative Intervention Does Not Change Pain Perception in Patients With Diabetic Foot Ulcers
Olivia V. Waldman, Stephanie P. Hao, Jeff R. Houck, Nicolette J. Lee, Judith F. Baumhauer, Irvin Oh
Clinical Diabetes Apr 2020, 38 (2) 132-140; DOI: 10.2337/cd19-0031
del.icio.us logo Digg logo Reddit logo Twitter logo CiteULike logo Facebook logo Google logo Mendeley logo

Jump to section