Outcomes of Conservative Treatment in Patients with Chronic Limb Threatening Ischaemia: A Systematic Review and Meta-Analysis.

OBJECTIVE: Chronic limb threatening ischaemia (CLTI) is the most severe form of peripheral arterial disease. International guidelines recommend arterial revascularisation in patients with CLTI. However, these patients are often fragile elderly people with significant comorbidities, whose vascular anatomy is not always suitable for open or endovascular revascularisation. Recent studies have suggested acceptable outcomes of conservative treatment. A systematic review of the available literature was conducted to obtain best estimates of outcomes of conservative treatment in patients with CLTI. DATA SOURCES: MEDLINE, Embase, and Cochrane Central. REVIEW METHODS: A systematic review and meta-analysis was carried out following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. MEDLINE, Embase, and Cochrane Central were searched from inception until September 2019. All studies reporting on outcomes of conservative treatment for CLTI were considered. Study selection, data extraction, and risk of bias assessment were done by two investigators independently. Risk of bias was evaluated with a modified version of the Cochrane tool for observational studies. Outcomes of interest were all cause mortality, major amputation, and amputation free survival (AFS) after at least 12 months of follow up. A random effects model was used for meta-analyses. RESULTS: Twenty-seven publications were included, consisting of 12 observational studies and 15 placebo arms from randomised clinical trials, totalling 1 642 patients. Most studies included patients with non-reconstructable CLTI. Overall study quality was moderate. The pooled 12 month all cause mortality rate in 14 studies comprising 1 003 patients was 18% (95% confidence interval [CI] 13 - 25, I2 = 73%). The pooled major amputation rate from 14 studies comprising 755 patients was 27% (95% CI 20 - 36, I2 = 65%) after one year, and pooled AFS rate after 12 months in 11 studies with 970 patients was 60% (95% CI 52 - 67, I2 = 75%). CONCLUSION: Conservative treatment for patients with CLTI may be considered and does not always result in loss of limb or patient demise. The results of this review can be used to inform patients with CLTI about conservative treatment as part of a shared decision making process.

Clinical Analytics Prediction Engine (CAPE): Development, electronic health record integration and prospective validation of hospital mortality, 180-day mortality and 30-day readmission risk prediction models.

BACKGROUND: Numerous predictive models in the literature stratify patients by risk of mortality and readmission. Few prediction models have been developed to optimize impact while sustaining sufficient performance. OBJECTIVE: We aimed to derive models for hospital mortality, 180-day mortality and 30-day readmission, implement these models within our electronic health record and prospectively validate these models for use across an entire health system. MATERIALS & METHODS: We developed, integrated into our electronic health record and prospectively validated three predictive models using logistic regression from data collected from patients 18 to 99 years old who had an inpatient or observation admission at NorthShore University HealthSystem, a four-hospital integrated system in the United States, from January 2012 to September 2018. We analyzed the area under the receiver operating characteristic curve (AUC) for model performance. RESULTS: Models were derived and validated at three time points: retrospective, prospective at discharge, and prospective at 4 hours after presentation. AUCs of hospital mortality were 0.91, 0.89 and 0.77, respectively. AUCs for 30-day readmission were 0.71, 0.71 and 0.69, respectively. 180-day mortality models were only retrospectively validated with an AUC of 0.85. DISCUSSION: We were able to retain good model performance while optimizing potential model impact by also valuing model derivation efficiency, usability, sensitivity, generalizability and ability to prescribe timely interventions to reduce underlying risk. Measuring model impact by tying prediction models to interventions that are then rapidly tested will establish a path for meaningful clinical improvement and implementation.