The effectiveness of dextrose prolotherapy in plantar fasciitis: A systemic review and meta-analysis.

BACKGROUND: Dextrose prolotherapy (DPT) is considered to be a type of regenerative therapy and is widely used in various musculoskeletal disorders. Plantar fasciitis is a common cause of heel pain that affects the quality of life of many people. We aimed to evaluate the effectiveness and safety of DPT for plantar fasciitis. METHODS: PubMed, Embase, and the Cochrane Library were searched from their respective inception dates to June 2021. Only randomized controlled trials comparing DPT and other interventions for plantar fasciitis were included in this review. Standardized mean differences (SMDs) with 95% confidence intervals were calculated for comparison. The outcome measurements included visual analog score, numeric rating scale, Foot Function index, Revised Foot Function index, American Orthopedic Foot and Ankle Score, and plantar fascia thickness. Post-treatment duration was classified as short-term (1-2 months), medium-term (3 months), or long-term (6 months). RESULTS: Six studies with 388 adult patients diagnosed with plantar fasciitis were included for the meta-analysis. In terms of pain scores improvement, DPT was superior to placebo or exercise in the short-term (SMD: -1.163, 95%CI: -2.17 to -0.156) and the medium-term (SMD: -1.394, 95%CI: -2.702 to -0.085). DPT was inferior to corticosteroid injection in the short-term (SMD: 0.781, 95%CI: 0.41 to 1.152). For functional improvement, DPT was superior to placebo or exercise in the short-term (SMD: -1.51, 95%CI: -2.96 to -0.059), but inferior to corticosteroid injection (SMD: 0.526, 95%CI: 0.161 to 0.89) and extracorporeal shock wave therapy in the short-term (SMD: 0.484, 95%CI: 0.145 to 0.822). Randomized controlled trials showed a better pain improvement in the long-term for patients treated with DPT compared to corticosteroid (P = .002) and exercise control (P < .05). No significant differences were found between patients treated with DPT and patients treated with platelet-rich plasma. CONCLUSION: Dextrose prolotherapy was a safe and effective treatment option for plantar fasciitis that may have long-term benefits for patients. The effects were comparable to extracorporeal shock wave therapy or platelet-rich plasma injection. Further studies with standardized protocols and long-term follow-up are needed to address potential biases.

Assessing the Prognostic Significance of Tumor-Infiltrating Lymphocytes in Patients With Melanoma Using Pathologic Features Identified by Natural Language Processing.

Importance: Although tumor-infiltrating lymphocytes (TILs) are an important histopathologic characteristic reflecting host immune response in patients with melanoma, their prognostic value remains controversial. Because manual review of medical records is labor intensive, a survival analysis using a large patient cohort with comprehensive clinical and histopathologic characteristics is lacking. Objective: To assess the prognostic significance of TILs among patients with cutaneous melanoma using a large cohort established through natural language processing (NLP) algorithms. Design, Setting, and Participants: This retrospective cohort study analyzed the medical records of 14 436 patients with cutaneous melanoma at Brigham and Women's Hospital between June 1, 2004, and December 31, 2019. Patients were followed up to death or censored at their last clinical visit. Main Outcome and Measures: The primary outcome was overall survival (OS). Survival analysis was conducted using Kaplan-Meier curves, the log-rank test, and Cox proportional hazards regression analysis. Results: A total of 14 436 patients with cutaneous melanoma were identified in the institution's pathology information system. Using NLP, we established a study cohort of 2624 patients (1462 men [55.7%]; median age, 61 years [interquartile range, 50-72 years]) who had vertical growth phase melanoma with TIL status scored. Absent TILs were identified in 434 patients (16.5%), nonbrisk TILs in 1916 patients (73.0%), and brisk TILs in 274 patients (10.4%). The 5-year survival rate was 71.0% (95% CI, 65.5%-76.9%) among patients with an absence of TILs, 73.8% (95% CI, 71.1%-76.5%) among patients with nonbrisk TILs, and 85.2% (95% CI, 80.0%-90.7%) among patients with brisk TILs. Brisk TILs were significantly associated with improved OS (adjusted hazard ratio, 0.63; 95% CI, 0.42-0.95; P = .03; 14.2% OS advantage at 5 years), and nonbrisk TILs were not associated with improved OS (adjusted hazard ratio, 0.87; 95% CI, 0.68-1.11; P = .25), compared with the absence of TILs. Conclusions and Relevance: This study provides evidence based on a large patient cohort from a single institution that suggests that brisk TILs represent an independent prognostic factor for OS among patients with primary cutaneous melanoma. The study also suggests that NLP is a highly efficient tool to facilitate large-scale analyses that involve free-text clinical data.

A novel method to retrieve alerts from a homegrown Computerized Physician Order Entry (CPOE) system of an academic medical center: Comprehensive alert characteristic analysis.

BACKGROUND: The collection and analysis of alert logs are necessary for hospital administrators to understand the types and distribution of alert categories within the organization and reduce alert fatigue. However, this is not readily available in most homegrown Computerized Physician Order Entry (CPOE) systems. OBJECTIVE: To present a novel method that can collect alert information from a homegrown CPOE system (at an academic medical center in Taiwan) and conduct a comprehensive analysis of the number of alerts triggered and alert characteristics. METHODS: An alert log collector was developed using the Golang programming language and was implemented to collect all triggered interruptive alerts from a homegrown CPOE system of a 726-bed academic medical center from November 2017 to June 2018. Two physicians categorized the alerts from the log collector as either clinical or non-clinical (administrative). RESULTS: Overall, 1,625,341 interruptive alerts were collected and classified into 1,474 different categories based on message content. The sum of the top 20, 50, and 100 categories of most frequently triggered alerts accounted for approximately 80, 90 and 97 percent of the total triggered alerts, respectively. Among alerts from the 100 most frequently triggered categories, 1,266,818 (80.2%) were administrative and 312,593 (19.8%) were clinical alerts. CONCLUSION: We have successfully developed an alert log collector that can serve as an extended function to retrieve alerts from a homegrown CPOE system. The insight generated from the present study could also potentially bring value to hospital system designers and hospital administrators when redesigning their CPOE system.

Assessing the International Transferability of a Machine Learning Model for Detecting Medication Error in the General Internal Medicine Clinic: Multicenter Preliminary Validation Study.

BACKGROUND: Although most current medication error prevention systems are rule-based, these systems may result in alert fatigue because of poor accuracy. Previously, we had developed a machine learning (ML) model based on Taiwan's local databases (TLD) to address this issue. However, the international transferability of this model is unclear. OBJECTIVE: This study examines the international transferability of a machine learning model for detecting medication errors and whether the federated learning approach could further improve the accuracy of the model. METHODS: The study cohort included 667,572 outpatient prescriptions from 2 large US academic medical centers. Our ML model was applied to build the original model (O model), the local model (L model), and the hybrid model (H model). The O model was built using the data of 1.34 billion outpatient prescriptions from TLD. A validation set with 8.98% (60,000/667,572) of the prescriptions was first randomly sampled, and the remaining 91.02% (607,572/667,572) of the prescriptions served as the local training set for the L model. With a federated learning approach, the H model used the association values with a higher frequency of co-occurrence among the O and L models. A testing set with 600 prescriptions was classified as substantiated and unsubstantiated by 2 independent physician reviewers and was then used to assess model performance. RESULTS: The interrater agreement was significant in terms of classifying prescriptions as substantiated and unsubstantiated (κ=0.91; 95% CI 0.88 to 0.95). With thresholds ranging from 0.5 to 1.5, the alert accuracy ranged from 75%-78% for the O model, 76%-78% for the L model, and 79%-85% for the H model. CONCLUSIONS: Our ML model has good international transferability among US hospital data. Using the federated learning approach with local hospital data could further improve the accuracy of the model.