Automated Detection of Patient Harm: Implementation and Prospective Evaluation of a Real-Time Broad-Spectrum Surveillance Application in a Hospital With Limited Resources.

OBJECTIVES: This study aimed to prospectively validate an application that automates the detection of broad categories of hospital adverse events (AEs) extracted from a basic hospital information system, and to efficiently mobilize resources to reduce the level of acquired patient harm. METHODS: Data were collected from an internally designed software, extracting results from 14 triggers indicative of patient harm, querying clinical and administrative databases including all inpatient admissions (n = 8760) from October 2019 to June 2020. Representative samples of the triggered cases were clinically validated using chart review by a consensus expert panel. The positive predictive value (PPV) of each trigger was evaluated, and the detection sensitivity of the surveillance system was estimated relative to incidence ranges in the literature. RESULTS: The system identified 394 AEs among 946 triggered cases, associated with 291 patients, yielding an overall PPV of 42%. Variability was observed among the trigger PPVs and among the estimated detection sensitivities across the harm categories, the highest being for the healthcare-associated infections. The median length of stay of patients with an AE showed to be significantly higher than the median for the overall patient population. CONCLUSIONS: This application was able to identify AEs across a broad spectrum of harm categories, in a real-time manner, while reducing the use of resources required by other harm detection methods. Such a system could serve as a promising patient safety tool for AE surveillance, allowing for timely, targeted, and resource-efficient interventions, even for hospitals with limited resources.

The Use of Medical Imaging Request Forms as Trigger Tools to Detect Intra-Hospital Adverse Events: A Pilot Study.

Aim: To evaluate the contribution of medical imaging request forms as trigger tools to detect patient adverse event (AE) occurring during hospitalization. Material and Methods: This is a retrospective study in a single institution. Between January and June 2019, the hospital information system (HIS) was fetched for request forms of radiological examinations performed for inpatients >48 hours after the admission date. The investigated request forms were: Doppler ultrasound of the upper limbs, Doppler ultrasound of the lower limbs, and the repetition of three consecutive requests of chest radiographs within 24 hrs, to detect upper or lower limb venous thrombosis, or AEs related to the respiratory system, respectively. Patients' medical charts and radiological examinations were evaluated to document the presence or absence of an AE. The frequencies of AEs in the three groups of trigger tools were compared to corresponding control groups, matched according to age, sex and length of stay. Results: Among a total of 2798 hospital admissions during the study period, there were 74 files triggered by the three types of radiological request forms. There were 6/24 AE (25%) related to upper limb venous thrombosis, 4/33 (12.1%) AE related to lower limb venous thrombosis, and 6/17 (35.3%) AE related to the respiratory system. For all the trigger tools, the frequency of AE in the study groups was significantly higher than that in the control groups. Conclusion: Medical imaging requests could be used as potential trigger tools to detect adverse events related to hospital stay.

Early Prediction of All-Cause Clinical Deterioration in General Wards Patients: Development and Validation of a Biomarker-Based Machine Learning Model Derived From Rapid Response Team Activations.

OBJECTIVE: The aim of the study is to evaluate the performance of a biomarker-based machine learning (ML) model (not including vital signs) derived from reviewed rapid response team (RRT) activations in predicting all-cause deterioration in general wards patients. DESIGN: This is a retrospective single-institution study. All consecutive adult patients' cases on noncritical wards identified by RRT calls occurring at least 24 hours after patient admission, between April 2018 and June 2020, were included. The cases were reviewed and labeled for clinical deterioration by a multidisciplinary expert consensus panel. A supervised learning approach was adopted based on a set of biomarkers and demographic data available in the patient's electronic medical record (EMR). SETTING: The setting is a 250-bed tertiary university hospital with a basic EMR, with adult (>18 y) patients on general wards. PATIENTS: The study analyzed the cases of 514 patients for which the RRT was activated. Rapid response teams were extracted from the hospital telephone log data. Two hundred eighteen clinical deterioration cases were identified in these patients after expert chart review and complemented by 146 "nonevent" cases to build the training and validation data set. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: The best performance was achieved with the random forests algorithm, with a maximal area under the receiver operating curve of 0.90 and F1 score of 0.85 obtained at prediction time T0-6h, slightly decreasing but still acceptable (area under the receiver operating curve, >0.8; F1 score, >0.75) at T0-42h. The system outperformed most classical track-and-trigger systems both in terms of prediction performance and prediction horizon. CONCLUSIONS: In hospitals with a basic EMR, a biomarker-based ML model could be used to predict clinical deterioration in general wards patients earlier than classical track-and-trigger systems, thus enabling appropriate clinical interventions for patient safety and improved outcomes.

Evaluation of a Broad-Spectrum Partially Automated Adverse Event Surveillance System: A Potential Tool for Patient Safety Improvement in Hospitals With Limited Resources.

OBJECTIVE: The aim of the study was to evaluate the sensitivity and resource efficiency of a partially automated adverse event (AE) surveillance system for routine patient safety efforts in hospitals with limited resources. METHODS: Twenty-eight automated triggers from the hospital information system's clinical and administrative databases identified cases that were then filtered by exclusion criteria per trigger and then reviewed by an interdisciplinary team. The system, developed and implemented using in-house resources, was applied for 45 days of surveillance, for all hospital inpatient admissions (N = 1107). Each trigger was evaluated for its positive predictive value (PPV). Furthermore, the sensitivity of the surveillance system (overall and by AE category) was estimated relative to incidence ranges in the literature. RESULTS: The surveillance system identified a total of 123 AEs among 283 reviewed medical records, yielding an overall PPV of 52%. The tool showed variable levels of sensitivity across and within AE categories when compared with the literature, with a relatively low overall sensitivity estimated between 21% and 44%. Adverse events were detected in 23 of the 36 AE categories defined by an established harm classification system. Furthermore, none of the detected AEs were voluntarily reported. CONCLUSIONS: The surveillance system showed variable sensitivity levels across a broad range of AE categories with an acceptable PPV, overcoming certain limitations associated with other harm detection methods. The number of cases captured was substantial, and none had been previously detected or voluntarily reported. For hospitals with limited resources, this methodology provides valuable safety information from which interventions for quality improvement can be formulated.

Comparison of Machine Learning Algorithms for Classifying Adverse-Event Related 30-Day Hospital Readmissions: Potential Implications for Patient Safety.

Studies in the last decade have focused on identifying patients at risk of readmission using predictive models, in an objective to decrease costs to the healthcare system. However, real-time models specifically identifying readmissions related to hospital adverse-events are still to be elaborated. A supervised learning approach was adopted using different machine learning algorithms based on features available directly from the hospital information system and on a validated dataset elaborated by a multidisciplinary expert consensus panel. Accuracy results upon testing were in line with comparable studies, and variable across algorithms, with the highest prediction given by Artificial Neuron Networks. Features importances relative to the prediction were identified, in order to provide better representation and interpretation of results. Such a model can pave the way to predictive models for readmissions related to patient harm, the establishment of a learning platform for clinical quality measurement and improvement, and in some cases for an improved clinical management of readmitted patients.

Antitumor activities of the synthetic retinoid ST1926 in two-dimensional and three-dimensional human breast cancer models.

Despite recent advances in chemotherapy, aggressive and metastatic breast cancers remain refractory to targeted therapy and the development of novel drugs is urgently needed. Retinoids are crucial regulators of cellular proliferation, differentiation, and cell death, and have shown potent chemotherapeutic and chemopreventive properties. The major drawback of the use of all-trans retinoic acid (ATRA) in cancer therapy is disease relapse. Therefore, synthetic retinoids, specifically ST1926, have emerged as potent anticancer agents. Given the importance of the microenvironment in modulating the response of cancer cells to chemotherapeutic drugs, we investigated the antitumor activities of ST1926 in two-dimensional (2D) and different three-dimensional (3D) human breast cancer models and compared them with ATRA. We have shown that in 2D cell culture models, ATRA-resistant MCF-7 and MDA-MB-231 cells were sensitive to ST1926 at submicromolar concentrations that spared the 'normal-like' breast epithelial cells. ST1926 induced apoptosis and S-phase arrest, caused DNA damage, and downregulated the Wnt/ß-catenin pathway in breast cancer cells in 2D and 3D cell culture models. ST1926-mediated growth inhibition was independent of the retinoid receptor-signaling pathway. Long-term treatments with low submicromolar ST1926 concentrations reduced the anchorage-independent growth and decreased the sphere-forming ability of breast cancer progenitor cells in the sphere formation assay. Furthermore, ST1926 potently induced cell death of breast cancer cells under 3D conditions and spared the lumen-forming ability of normal-like breast epithelial cells. In tested 3D models, ATRA had minimal effects on the growth of breast cancer cells compared with ST1926. In summary, our results highlight the therapeutic potential of ST1926 in breast cancer and warrant its further clinical development.

Inhibition of tumor promotion by parthenolide: epigenetic modulation of p21.

The promotion stage in the multistep process of epidermal tumorigenesis is NF-кB-dependent, epigenetically regulated, and reversible, thus, a suitable target for chemoprevention. We investigated whether the NF-кB inhibitor, parthenolide, currently in cancer clinical trials, attenuates tumor promotion by modulating the epigenetically regulated NF-кB target genes, p21 and cyclin D1. Parthenolide selectively inhibited the growth of neoplastic keratinocytes while sparing normal ones. Specifically, in JB6P+ cells, a model of tumor promotion, noncytotoxic parthenolide concentrations abrogated tumor promoter-induced cell proliferation and anchorage-independent growth. Furthermore, parthenolide decreased tumor promoter-induced NF-кB activity, increased p21, and decreased cyclin D1 expression. In parthenolide-treated cells, p21 transcription correlated with relaxed chromatin and p65/NF-кB binding at the p21 promoter. However, cyclin D1 transcription correlated more with p65/NF-кB binding than with chromatin structure at the cyclin D1 promoter. Epigenetic regulation by parthenolide seemed specific, as parthenolide did not alter global histone acetylation and methylation and histone deacetylase activity. Because p21 expression by parthenolide was sustained, we used p21-siRNA and p21 -/- cancer cells and showed that the loss of p21 is cytoprotective against parthenolide. Low parthenolide concentrations (0.25 mg/kg) inhibited tumor growth of promoted JB6P+ cells in xenograft immunocompromised mice using two different chemoprevention protocols. Tissue microarray of mouse tumors showed that parthenolide decreased scores of the cell proliferation marker Ki67 and p65/NF-кB, whereas it increased p21 expression. These results show that low doses of parthenolide inhibit tumor promotion and epigenetically modulate p21 expression, highlighting the potential role of this drug as a chemopreventive agent and in epigenetic cancer therapy.

Sesquiterpene lactones isolated from indigenous Middle Eastern plants inhibit tumor promoter-induced transformation of JB6 cells.

BACKGROUND: Sesquiterpene lactones (SL) are plant secondary metabolites that are known for their anti-fungal, anti-bacterial, anti-inflammatory, and anti-tumor properties. Considering that several SL-derived drugs are currently in cancer clinical trials, we have tested two SL molecules, 3-ß-methoxy-iso-seco-tanapartholide (ß-tan) isolated from Achillea falcata and salograviolide A (Sal A) isolated from Centaurea ainetensis, for their anti-tumor properties. We used the mouse epidermal JB6P + cells as a model for tumor promotion and cellular transformation. Key players that are involved in cellular transformation and tumorigenesis are the AP-1 and NF-κB transcription factors; therefore, we assessed how ß-tan and Sal A modulate their signaling pathways in JB6P + cells. METHODS: The effects of ß-tan and Sal A on the growth of normal and neoplastic keratinocytes and on the tumor promotion-responsive JB6P + cells were determined using the MTT assay. Anchorage-independent cell growth transformation assays were used to evaluate the anti-tumor promoting properties of these SL molecules in JB6P + cells and dual luciferase reporter assays and western blot analysis were used to investigate their effects on tumor promoter-induced AP-1 and NF-κB activities and protein levels of key AP-1 and NF-кB target genes. RESULTS: ß-tan and Sal A selectively inhibited tumor promoter-induced cell growth and transformation of JB6P + cells at concentrations that do not affect JB6P + and primary keratinocytes basal cell growth. In addition, both molecules reduced basal and tumor promoter-induced NF-κB transcriptional activities, differentially regulated basal and tumor promoter-induced AP-1 transcriptional activities, and modulated key players of the AP-1 and NF-κB signaling pathways. CONCLUSIONS: These results highlight the anti-tumor promoting properties of ß-tan and Sal A. These SL molecules isolated from two plant species native to the Middle East may provide opportunities for complementary medicine practices.

Epigenetic mechanisms of plant-derived anticancer drugs.

Epigenetic mechanisms are essential for normal development and maintenance of adult life. Disruption of epigenetic processes results in deregulated gene expression and leads to life-threatening diseases, in particular, cancer. Global epigenetic alterations are a hallmark of cancer. Cancer epigenetics revealed the deregulation of all components of the epigenetic machinery including DNA methylation, histone modifications, chromatin structure, and non-coding RNAs. Drugs targeting epigenetic processes, or "epi-drugs", are at the forefront of drug discovery, and plant-derived compounds have shown promise. Most of the plant-derived anticancer drugs that work through epigenetic mechanisms are polyphenols; the others are alkaloids, organosulfur compounds, and terpenoids. This review focuses on the epigenetic machinery and its basis for cancer therapy, highlights plant-derived anticancer drugs with epigenetic mechanisms of action, and discusses their potential use in epigenetic therapy.