The Development and Usability of the AMPREDICT Decision Support Tool: A Mixed Methods Study

The role of non-invasive vascular assessment prior to lower extremity amputation

ObjectiveTo compare outcomes between patients who underwent preoperative non-invasive testing and those who did not prior to all-level lower extremity amputation (LEA).MethodsA retrospective analysis of patients undergoing LEA between April 1st 2019 and June 30th 2023 at an acute care facility was performed and relevant demographic and perioperative data collected. The primary endpoint was the association of preoperative non-invasive testing on MALE and MACE.Results188 patients who underwent all-level LEA were included and stratified into two groups: those who had preoperative non-invasive testing (52.7%; n = 99; p < .01) and those who did not (Groups A and B, respectively). Group A demonstrated higher minority representation (p = .04), pre-existing vascular disease (p < .01), hypertension (p < .01), and renal and cardiac comorbidities (both p < .01).Chi-squared analysis between groups demonstrated no significant difference in all-level LEA for outcomes of postoperative revascularization (p = .63), re-amputation (major or all-level; p = .98 and p = .78, respectively), nor any differences in wound complications (p = .79) or mortality (p = .37). In sub-analyses for major and minor amputations, there remained no significant differences in major re-amputation (p = .69 and p = .27, respectively), 30-day wound complications (p = .44 and p = .65, respectively), or MACE (p = .50 and p = .93, respectively) between groups.ConclusionsAuthors note infrequent use of non-invasive testing prior to LEA, and similar MALE and MACE outcomes between groups with potential benefit in medically vulnerable cohorts. With a lack of established guidelines on preoperative workup prior to LEA, additional prospective studies with matched cohorts and similar endpoints may promote algorithms to optimize perioperative outcomes.

Applying mobility prediction models to real-world patients with major amputations

BACKGROUND

Outcome prediction models (PMs) have become commonplace and are promoted to aid in counseling patients. The aim of this study is to evaluate the performance of existing mobility PMs for post-major amputation (MA) patients in a real-world, socioeconomically disadvantaged population.

METHODS

A retrospective review of patients with MA secondary to peripheral arterial disease from 2016 to 2022 was performed. Patients who were nonambulatory before MA or with contralateral MA were excluded. Three published PMs were investigated: (1) AmpPredict (predicts 1-year mobility), (2) Amputee Single Item Mobility Measure, (predicts degree of mobility with prosthesis at 1 year), both derived from Veteran's Affairs data, and (3) a Vascular Quality Initiative (VQI) data-derived model (predicts 1-year mobility). Predicted mobility rates vs actual mobility rates were compared.

RESULTS

The study cohort consisted of 126 patients, 71% male, 60% non-White race, with a mean state Area Deprivation Index of 9 of 10. Baseline characteristics were significantly different between the study and derivation cohorts. Actual mobility at 1 year was 43%. Of the 38 patients with an AmpPredict 1-year mobility of ≥70%, 45% actually achieved mobility. Of 101 patients with a high predicted probability from the VQI score (≥71%), 48% achieved mobility. The mean difference between AmpPredict and VQI for a given patient was 36% (range, 1%-81%). The Amputee Single Item Mobility Measure predicted 87% of patients would be community (vs home) ambulators at 1 year and 32% of patients actually achieved community ambulation (sensitivity of 91%, specificity of 14%, positive predictive value of 33%, and negative predictive value of 79%).

CONCLUSIONS

Published models dramatically overestimated the likelihood of mobility in our patient cohort. This result may be related to the demographics and comorbidities of our cohort being significantly different from the derivation cohorts. We recommend caution when applying PMs to a population with significantly different characteristics from the population used to derive the model.

AMPREDICT MoRe: Predicting Mortality and Re-amputation Risk after Dysvascular Amputation

OBJECTIVE

This study aimed to create a novel prediction model (AMPREDICT MoRe) that predicts death and re-amputation after dysvascular amputation, which overcomes prior implementation barriers by using only predictors that are readily available in the electronic health record (EHR).

METHODS

This was a retrospective cohort study of 9 221 patients with incident unilateral transmetatarsal, transtibial, or transfemoral amputation secondary to diabetes and or peripheral arterial disease identified in the Veterans Affairs Corporate Data Warehouse between 1 October 2015 and 30 September 2021. The prediction model evaluated factors falling into several key domains: prior revascularisation; amputation level; demographics; comorbidities; mental health; health behaviours; laboratory values; and medications. The primary outcome included four categories: (i) no death and no re-amputation (ND/NR); (ii) no death and re-amputation (ND/R); (iii) death and no re-amputation (D/NR); and (iv) death and re-amputation (D/R). Multinomial logistic regression was used to fit one year post-incident amputation risk prediction models. Variable selection was performed using LASSO (least absolute shrinkage and selection operator), a machine learning methodology. Model development was performed using a randomly selected 80% of the data, and the final model was externally validated using the remaining 20% of subjects.

RESULTS

The final prediction model included 23 predictors. The following outcome distribution was observed in the development sample: ND/NR, n = 4 254 (57.7%); ND/R, n = 1 690 (22.9%); D/NR, n = 1 056 (14.3%); and D/R, n = 376 (5.1%). The overall discrimination of the model was moderately strong (M index 0.70), but a deeper look at the c indices indicated that the model had better ability to predict death than re-amputation (ND/NR vs. ND/R, 0.64; ND/NR vs. D/NR, 0.78; grouped ND vs. D, 0.79 and NR vs. R, 0.67). The model was best at distinguishing individuals with no negative outcomes vs. both negative outcomes (ND/NR vs. D/R, 0.82).

CONCLUSION

The AMPREDICT MoRe model has been successfully developed and validated, and can be applied at the time of amputation level decision making. Since all predictors are available in the EHR, a future decision support tool will not require patient interview.

Usability of a Cloud-Based Home Healthcare Client Monitoring Platform: A Simulation-Based Approach

PURPOSE

The aim of this study was to assess the usability of a cloud-based home healthcare monitoring platform (CHHM).

DESIGN

A proof of concept using a simulated client scenario was used in this study.

METHODS

Using a mixed-methods approach, a convenience sample of 14 nursing students was used to assess the usability of CHHM during a simulation. Students engaged in evaluation using two questionnaires: the Post-Study System Usability Questionnaire (Usability Questionnaire) and a researcher-developed questionnaire incorporating qualitative responses.

RESULTS

The overall mean score of the Usability Questionnaire was 2.91, with the range of 7, indicating the usability of the platform. The Usability Questionnaire was negatively scored, with 1 being the best. The means of the two subscales, System Usefulness (2.77) and Information Quality (3.26), illustrated the platform's solid performance. The Interface Quality subscale mean was 2.94, indicating a lower level of satisfaction. Qualitative data from the researcher-developed questionnaire indicated that participants found the platform straightforward, clear, and user-friendly, with the potential to enhance practice.

CLINICAL RELEVANCE

This platform may enhance rehabilitation nurses' ability to monitor clients at home.

CONCLUSION

The CHHM can support and enhance the delivery of home health care by enabling continuous remote monitoring. By incorporating user-centered design principles, cloud-native architecture, and wearable sensors, we demonstrated a proof of concept and laid the foundation for further platform development.

Evaluating the Prognostic Accuracy of AMPREDICT in Predicting 1-Year Mortality Following Major Lower Limb Amputation

BACKGROUND

Accurately predicting postoperative outcomes is fundamental to informed clinical decision-making, and alignment of patient and family expectations. The AMPREDICT Decision Support Tool is a predictive tool designed to assess the probability of mortality 1 year after major and minor amputations. We aimed to evaluate the prognostic accuracy of AMPREDICT in our Veteran patient population.

METHODS

Retrospective review of lower extremity amputations completed at the West Los Angeles Veterans Affairs hospital from 2000 to 2020. Staged open amputations and previous minor amputations were excluded. Using the AMPREDICT tool, the probability of mortality 1 year postsurgery for single-stage transfemoral and transtibial amputations was calculated, then compared with observed patient outcomes. Observed to predicted mortality was compared through boxplots, at 1 year after surgery, confidence intervals were calculated, and group means were compared using Student's t-test. Receiver operator curves were constructed to assess discriminatory capacity of the tool. Significance was set at P < 0.05.

RESULTS

Four hundred twenty three patients underwent 650 lower extremity amputations during our study period. Two hundred sixty seven patients underwent single-stage transfemoral or transtibial amputations comprising our study cohort. The average age at amputation was 66 years with an average age of death at 71 years. AMPREDICT tool's prognostic capability varied across the 2 amputations studied. For single-staged transfemoral amputations, prediction aligned closely with observed outcomes, as indicated by a significant P value of 0.0002 (confidence interval 12.73-36.37). For single-stage transtibial amputations, the predictions were also significant, P value 0.0017 (confidence interval 5.25-21.20), although had a wider prediction range.

CONCLUSIONS

Our study confirms the reliability of the AMPREDICT tool in predicting 1-year mortality for patients undergoing major lower limb amputations. The predictive accuracy was found to be statistically significant for both single-staged transfemoral and transtibial amputations. These findings suggest that AMPREDICT may be a valuable tool in the clinical setting for patients undergoing major lower limb amputation.

Development and alpha testing of a patient shared decision aid for prosthesis design for new lower limb prosthesis users

BACKGROUND

After lower limb amputation, several prosthesis design options exist. However, prosthesis design decisions do not always reflect a prosthesis user's needs, values, and preferences.

OBJECTIVE

To develop a patient decision aid (PDA) prototype for prosthetists and new prosthesis users facing prosthesis design decisions after lower limb amputation, and to assess its usability, accuracy, and comprehensibility.

STUDY DESIGN

Exploratory mixed methods.

METHODS

PDA development was informed by a qualitative needs assessment and guided by the International Patient Decision Aid Standards. The PDA was evaluated by steering groups of experienced prosthesis users and prosthetic professionals (prosthetists and researchers) to test usability, accuracy, and comprehensibility through focus groups, individual interviews, and rating on a Likert scale ranging from 1 to 10.

RESULTS

The resulting PDA included 6 sections: (1) Amputation and Early Recovery, (2) Communication, (3) Values, (4) Prosthesis Design, (5) Preferences, and (6) Prosthetic Journey. Usability, accuracy, and comprehensibility were rated as 9.2, 9.6, and 9.6, respectively, by prosthetic professionals, and 9.4, 9.6, and 9.6, respectively, by prosthesis users.

DISCUSSION

The PDA incorporated guidance by relevant stakeholders and was rated favorably, emphasizing a need for shared decision-making support in prosthesis design. One challenge was determining the amount of information in the PDA, highlighting the diversity in end users' informational needs. Future iterations of the PDA should undergo beta testing in clinical settings.

CONCLUSIONS

A standardized, iterative method was used to develop a PDA for new lower limb prosthesis users and prosthetists when considering prosthesis design decisions. The PDA was considered useable, accurate, and comprehensible.

Development of the AMPDECIDE Decision Aid to Facilitate Shared Decision Making in Patients Facing Amputation Secondary to Chronic Limb Threatening Ischemia

INTRODUCTION

We developed a patient decision aid to enhance patient participation in amputation level decision making when there is a choice between a transmetatarsal or transtibial amputation.

METHODS

In accordance with International Patient Decision Aid Standards, we developed an amputation level patient decision aid for patients who are being considered for either a transmetatarsal or transtibial amputation, incorporating qualitative literature data, quantitative literature data, qualitative provider and patient interviews, expert panel input and iterative patient feedback.

RESULTS

The rapid qualitative literature review and qualitative interviews identified five domains outcome priority domains important to patients facing amputation secondary to chronic limb threatening ischemia: 1) the ability to walk, 2) healing and risk for reamputation, 3) rehabilitation program intensity, 4) ease of prosthetic use, and 5) limb length after amputation. The rapid quantitative review identified only two domains with adequate evidence comparing differences in outcomes between the two amputation levels: mobility and reamputation. Patient, surgeon, rehabilitation and decision aid expert feedback allowed us to integrate critical facets of the decision including addressing the emotional context of loss of limb, fear and anxiety as an obstacle to decision making, shaping the decision in the context of remaining life years, and how to facilitate patient knowledge of value tradeoffs.

CONCLUSIONS

Amputation level choice is associated with significant outcome trade-offs. The AMPDECIDE patient decision aid can facilitate acknowledgment of patient fears, enhance knowledge of amputation level outcomes, assist patients in determining their personal outcome priorities, and facilitate shared amputation level decision making.

Mortality Conversations Between Male Veterans and Their Providers Prior to Dysvascular Lower Extremity Amputation

BACKGROUND

Among patients facing lower extremity amputation due to dysvascular disease, the mortality risk is very high. Given this, as well as the importance of a patient-centered approach to medical care, informing patients about their possible risk of dying may be important during preoperative shared decision-making. The goal of this investigation was to gain an understanding of patient and provider experiences discussing mortality within the context of amputation within the Veterans Health Administration.

METHODS

Semistructured interviews were performed with Veterans with peripheral arterial disease and/or diabetes, vascular and podiatric surgeons, and physical medicine and rehabilitation physicians. Interviews were analyzed using team-based content analysis to identify themes related to amputation-level decisions.

RESULTS

We interviewed 22 patients and 21 surgeons and physicians and identified 3 themes related to conversations around mortality: (1) both patients and providers report that mortality conversations are not common prior to amputation; (2) while most providers find value in mortality conversations, some express concerns around engaging in these discussions with patients; and (3) some patients perceive mortality conversations as unnecessary, but many are open to engaging in the conversation.

CONCLUSIONS

Providers may benefit from introducing the topic with patients, including providing the context for why mortality conversations may be valuable, with the understanding that patients can always decline to participate should they not be interested or comfortable discussing this issue.

AMPREDICT PROsthetics-Predicting Prosthesis Mobility to Aid in Prosthetic Prescription and Rehabilitation Planning

OBJECTIVE

To develop and validate a patient-specific multivariable prediction model that uses variables readily available in the electronic medical record to predict 12-month mobility at the time of initial post-amputation prosthetic prescription. The prediction model is designed for patients who have undergone their initial transtibial (TT) or transfemoral (TF) amputation because of complications of diabetes and/or peripheral artery disease.

DESIGN

Multi-methodology cohort study that identified patients retrospectively through a large Veteran's Affairs (VA) dataset then prospectively collected their patient-reported mobility.

SETTING

The VA Corporate Data Warehouse, the National Prosthetics Patient Database, participant mailings, and phone calls.

PARTICIPANTS

Three-hundred fifty-seven veterans who underwent an incident dysvascular TT or TF amputation and received a qualifying lower limb prosthesis between March 1, 2018, and November 30, 2020 (N=357).

INTERVENTIONS

Not applicable.

MAIN OUTCOME MEASURE

The Amputee Single Item Mobility Measure (AMPSIMM) was divided into a 4-category outcome to predict wheelchair mobility (0-2), and household (3), basic community (4), or advanced community ambulation (5-6).

RESULTS

Multinomial logistic lasso regression, a machine learning methodology designed to select variables that most contribute to prediction while controlling for overfitting, led to a final model including 23 predictors of the 4-category AMPSIMM outcome that effectively discriminates household ambulation from basic community ambulation and from advanced community ambulation-levels of key clinical importance when estimating future prosthetic demands. The overall model performance was modest as it did not discriminate wheelchair from household mobility as effectively.

CONCLUSIONS

The AMPREDICT PROsthetics model can assist providers in estimating individual patients' future mobility at the time of prosthetic prescription, thereby aiding in the formulation of appropriate mobility goals, as well as facilitating the prescription of a prosthetic device that is most appropriate for anticipated functional goals.

Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records

Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regression models (average AUCs 0.96, 0.91 and 0.81, respectively), P < 0.0001. Of interviewed physicians, 75% were receptive to an EHR-based automated PAD model. Feedback emphasized workflow optimization, including integrating risk assessments directly into the EHR, using dashboard designs that minimize clicks, and providing risk assessments for clinically complex patients. In conclusion, we demonstrate that EHR-based machine learning models can accurately detect risk of PAD and that physicians are receptive to automated risk detection for PAD. Future research aims to prospectively validate model performance and impact on patient outcomes.