Wound healing outcomes: Using big data and a modified intent‐to‐treat method as a metric for reporting healing rates

Ennis, William J.; Hoffman, Rachel A.; Gurtner, Geoffrey C.; Kirsner, Robert S.; Gordon, Hanna

doi:10.1111/wrr.12575

Cited by 15 publications

(14 citation statements)

References 27 publications

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“…Of the 100 patients, 77% healed, consistent with recent healing rates reported in the literature . Demographics of healed ulcers in patients were similar to overall demographics.…”

Section: Resultssupporting

confidence: 79%

“…This was independent of whether they had experienced a previous wound. Our data represent data from a university‐based specialty wound center, but healing rates are not significantly different from recent data from most wound centers, despite differences in wound etiology. Based on a logarithmic regression, the healing rates of chronic wounds have been estimated between 36 and 60% .…”

Section: Discussionmentioning

confidence: 69%

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Patients’ prediction of their wound healing time

Borda

Jaller

Kallis

et al. 2018

Wound Repair Regeneration

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Understanding and managing patients' expectations can help improve their adherence to treatment for chronic wounds; however, little is known concerning about their expectations regarding healing time. Recruited subjects were asked to predict how long their wounds would take to heal and their charts were reviewed to retrieve real time of healing. We recruited 100 subjects from which 77% have healed. Fifty-three subjects (68.8%) had a longer healing time than they predicted (underestimated), and 17 (22.1%) had a shorter healing time than they predicted (overestimated). Subjects with shorter wound duration history tended to predict shorter healing time than subjects with longer wound duration (p < 0.01). However, wound duration did not affect prediction accuracy (p = 0.65). Subjects with chronic wounds seem more often to underestimate their time of healing. Wound duration significantly influenced patients' prediction time, although it did not make their prediction more accurate. Patient education about expectations may be important as patients often expect their wounds to heal faster than they actually do.

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“…Of the 100 patients, 77% healed, consistent with recent healing rates reported in the literature . Demographics of healed ulcers in patients were similar to overall demographics.…”

Section: Resultssupporting

confidence: 79%

Section: Discussionmentioning

confidence: 69%

Patients’ prediction of their wound healing time

Borda

Jaller

Kallis

et al. 2018

Wound Repair Regeneration

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“…Fife et al discovered that healing rates publicly reported by 40 real‐world wound centers were very high at a mean of 92% (Table ). Ennis et al reviewed medical records at 626 wound clinics managed by the same company and found a mean healing rate of 74.6% (Table ). These examples of real‐world data may overestimate healing due to the lack of risk stratification, right censoring, and missing data information, as well as due to the effect of clustering of observations (when multiple providers/facilities are involved in the patients’ continuum of care) .…”

Section: The Need For a Reliable Wound Assessmentmentioning

confidence: 99%

Defining complete wound closure: Closing the gap in clinical trials and practice

Gould

2019

Wound Repair Regeneration

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We investigate how wound closure is determined in recent randomized controlled wound trials and real-world studies, identify solutions to the current limitations of wound assessment, and propose a standard methodology to define and assess wound healing in research. We searched PubMed for randomized clinical trials using the terms "complete wound closure" and "wound healing rate" and for real-world studies using the terms "real-world wound healing," "real-world wound data," and "wound registries" dating from March 2010 through March 2018. We selected studies that had "complete wound closure" or "healed wound" as an endpoint. Sixty-five trial articles and 10 real-world articles met our criteria, from which we extracted the wound type studied, definition of healed wound used, wound assessment method, the number of weeks assessed, the number of wounds, and the percent of healed wounds in the study group(s) and control group. There were 7,194 trial wounds included. The most common definition of healing used by 26 studies (40.6%) was complete/full/100% (re)epithelialization or closure without discharge, drainage/scab, and/or dressing. Fiftytwo studies (81.2%) used blinded wound assessment, and at least 10 studies (15.6%) used blinded adjudication. The real-world studies analyzed more than 901,396 wounds. Only three studies (33.3%) defined a healed/closed wound, two of which used "complete epithelialization." Eight studies (88.9%) did not define the wound assessment method; none indicated a blinded assessment. We support the Food and Drug Administration definition: 100% reepithelialization of the wound surface with no discernable exudate and without drainage or dressing, confirmed at two visits 2 weeks apart, and we recommend blinded adjudication for wound assessment. The widespread adoption of a standard wound healing definition and assessment method in wound care research would allow for stronger comparisons of treatment effects across studies to improve the evidence base and strengthen the treatment decisionmaking process in clinical practice.

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“…A detailed description of the staffing and skill mix of these centers has been published elsewhere. 21 In short, these centers are run by a wound care management company and staffed by combination of employed and contracted physicians, supported by specialized nurses and program managers. All clinicians are required to attend a 1-week specialty wound care training course and are provided evidence based algorithmic clinical practice guidelines.…”

Section: Datasetmentioning

confidence: 99%

“…Following Ennis et al, we applied a modified intent-to-treat approach in that wounds in patients lost to follow-up during the 12 weeks were considered not healed after excluding wounds that were followed for <7 days. 21 The determination of healing is made by the treating clinician during each visit based on the following criteria: (1) wound has zero wound measurements, that is, it is completely covered with a full layer of epithelium and no longer has exudate; (2) wound has received a flap procedure and presents post procedure with complete take; (3) wound has received a graft procedure and presents post procedure with complete success; and (4) wound margins have been approximated and sutured to facilitate closure and wound has zero measurements.…”

Section: Definition Of Outcomementioning

confidence: 99%

Development of a Model to Predict Healing of Chronic Wounds Within 12 Weeks

Cho

Mattke

Gordon³

et al. 2020

Advances in Wound Care

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Pré cis: Using both clinically informed and machine learning approaches, we developed a prediction model for chronic wound healing that can form the basis for quality measurement. Objective: Chronic wounds represent a highly prevalent but little recognized condition with substantial implications for patients and payers. While better wound care products and treatment modalities are known to improve healing rates, they are inconsistently used in real-world practice. Predicting healing rates of chronic wounds and comparing to actual rates could be used to detect and reward better quality of care. We developed a prediction model for chronic wound healing. Approach: We analyzed electronic medical records (EMRs) for 620,356 chronic wounds of various etiologies in 261,398 patients from 532 wound care clinics in the United States. Patient-level and wound-level parameters influencing wound healing were identified from prior research and clinician input. Logistic regression and classification tree models to predict the probability of wound healing within 12 weeks were developed using a random sample of 70% of the wounds and validated in the remaining data. Results: A total of 365,659 (58.9%) wounds were healed by week 12. The logistic and classification tree models predicted healing with an area under the curve of 0.712 and 0.717, respectively. Wound-level characteristics, such as location, area, depth, and etiology, were more powerful predictors than patient demographics and comorbidities. Innovation: The probability of wound healing can be predicted with reasonable accuracy in real-world data from EMRs. Conclusion: The resulting severity adjustment model can become the basis for applications like quality measure development, research into clinical practice and performance-based payment.

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Wound healing outcomes: Using big data and a modified intent‐to‐treat method as a metric for reporting healing rates

Cited by 15 publications

References 27 publications

Patients’ prediction of their wound healing time

Patients’ prediction of their wound healing time

Defining complete wound closure: Closing the gap in clinical trials and practice

Development of a Model to Predict Healing of Chronic Wounds Within 12 Weeks

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