scholarly journals Identifying weather variables as important clinical predictors of bacterial diarrhea among international travelers to Nepal and Thailand

2021 ◽  
Author(s):  
Melissa A. Pender ◽  
Timothy Smith ◽  
Ben J. Brintz ◽  
Prativa Pandey ◽  
Sanjaya Shrestha ◽  
...  
Keyword(s):  
Random Forest ◽  
Environmental Temperature ◽  
Prediction Rule ◽  
Clinical Decision ◽  
Patient Specific ◽  
Clinical Predictors ◽  
Weather Variables ◽  
Discriminative Performance ◽  
Support Tool ◽  
Bacterial Diarrhea

Background: Clinicians and travelers often have limited tools to differentiate bacterial from non-bacterial causes of travelers' diarrhea (TD). Development of a clinical prediction rule assessing the etiology of TD may help identify episodes of bacterial diarrhea and limit inappropriate antibiotic use. We aimed to identify predictors of bacterial diarrhea among clinical, demographic, and weather variables, as well as to develop and cross-validate a parsimonious predictive model. Methods: We collected de-identified clinical data from 457 international travelers with acute diarrhea presenting to two healthcare centers in Nepal and Thailand. We used conventional microbiologic and multiplex molecular methods to identify diarrheal etiology from stool samples. We used random forest and logistic regression to determine predictors of bacterial diarrhea. Results: We identified 195 cases of bacterial etiology, 63 viral, 125 mixed pathogens, 6 protozoal/parasite, and 68 cases without a detected pathogen. Random forest regression indicated that the strongest predictors of bacterial over viral or non-detected etiologies were average location-specific environmental temperature and RBC on stool microscopy. In 5-fold cross-validation, the parsimonious model with the highest discriminative performance had an AUC of 0.73 using 3 variables with calibration intercept -0.01 (SD 0.31) and slope 0.95 (SD 0.36). Conclusions: We identified environmental temperature, a location-specific parameter, as an important predictor of bacterial TD, among traditional patient-specific parameters predictive of etiology. Future work includes further validation and the development of a clinical decision-support tool to inform appropriate use of antibiotics in TD.

scholarly journals Adaptive design of a clinical decision support tool: What the impact on utilization rates means for future CDS research

2019 ◽  
Vol 5 ◽  
pp. 205520761982771 ◽  
Author(s):  
Devin Mann ◽  
Rachel Hess ◽  
Thomas McGinn ◽  
Rebecca Mishuris ◽  
Sara Chokshi ◽  
...  
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Prediction Rule ◽  
Clinical Prediction Rule ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Clinical Prediction ◽  
Support Tool ◽  
Clinical Decision Support Tool ◽  
User Centered

OBJECTIVE We employed an agile, user-centered approach to the design of a clinical decision support tool in our prior integrated clinical prediction rule study, which achieved high adoption rates. To understand if applying this user-centered process to adapt clinical decision support tools is effective in improving the use of clinical prediction rules, we examined utilization rates of a clinical decision support tool adapted from the original integrated clinical prediction rule study tool to determine if applying this user-centered process to design yields enhanced utilization rates similar to the integrated clinical prediction rule study. MATERIALS & METHODS: We conducted pre-deployment usability testing and semi-structured group interviews at 6 months post-deployment with 75 providers at 14 intervention clinics across the two sites to collect user feedback. Qualitative data analysis is bifurcated into immediate and delayed stages; we reported on immediate-stage findings from real-time field notes used to generate a set of rapid, pragmatic recommendations for iterative refinement. Monthly utilization rates were calculated and examined over 12 months. RESULTS We hypothesized a well-validated, user-centered clinical decision support tool would lead to relatively high adoption rates. Then 6 months post-deployment, integrated clinical prediction rule study tool utilization rates were substantially lower than anticipated based on the original integrated clinical prediction rule study trial (68%) at 17% (Health System A) and 5% (Health System B). User feedback at 6 months resulted in recommendations for tool refinement, which were incorporated when possible into tool design; however, utilization rates at 12 months post-deployment remained low at 14% and 4% respectively. DISCUSSION Although valuable, findings demonstrate the limitations of a user-centered approach given the complexity of clinical decision support. CONCLUSION Strategies for addressing persistent external factors impacting clinical decision support adoption should be considered in addition to the user-centered design and implementation of clinical decision support.

scholarly journals Stent placement for benign esophageal leaks, perforations, and fistulae: a clinical prediction rule for successful leakage control

Endoscopy ◽  
2017 ◽  
Vol 50 (02) ◽  
pp. 98-108 ◽  
Author(s):  
Emo van Halsema ◽  
Wouter Kappelle ◽  
Bas Weusten ◽  
Robert Lindeboom ◽  
Mark van Berge Henegouwen ◽  
...  
Keyword(s):  
Stent Placement ◽  
Predictive Value ◽  
Clinical Decision Making ◽  
Characteristic Curve ◽  
Model Performance ◽  
Prediction Rule ◽  
Clinical Decision ◽  
Validation Sample ◽  
Clinical Predictors ◽  
Predicted Probability

Abstract Background and study aims Sealing esophageal leaks by stent placement allows healing in 44 % – 94 % of patients. We aimed to develop a prediction rule to predict the chance of successful stent therapy. Patients and methods In this multicenter retrospective cohort study, patients with benign upper gastrointestinal leakage treated with stent placement were included. We used logistic regression analysis including four known clinical predictors of stent therapy outcome. The model performance to predict successful stent therapy was evaluated in an independent validation sample. Results We included etiology, location, C-reactive protein, and size of the leak as clinical predictors. The model was estimated from 145 patients (derivation sample), and 59 patients were included in the validation sample. Stent therapy was successful in 55.9 % and 67.8 % of cases, respectively. The predicted probability of successful stent therapy was significantly higher in success patients compared with failure patients in both the derivation (P < 0.001) and validation (P < 0.001) samples. The area under the receiver operating characteristic curve was 74.1 % in the derivation sample and 84.7 % in the validation sample. When the model predicted ≥ 70 % chance of success, the positive predictive value was 79 % in the derivation sample and 87 % in the validation sample. When the model predicted ≤ 50 % chance of success, the negative predictive value was 64 % and 86 %, respectively. Conclusions This prediction rule, consisting of four clinical predictors, could identify patients with esophageal leaks who were likely to benefit from or fail on stent therapy. The prediction rule can support clinical decision-making when the predicted probability of success is ≥ 70 % or ≤ 50 %.

Clinical characteristics of children evaluated for suspected pulmonary embolism with D-dimer testing

2017 ◽  
Vol 103 (9) ◽  
pp. 835-840 ◽  
Author(s):  
Jessica Kanis ◽  
Jonathan Pike ◽  
Cassandra L Hall ◽  
Jeffrey A Kline
Keyword(s):  
Pulmonary Embolism ◽  
Univariate Analysis ◽  
Haemoglobin Concentration ◽  
Prediction Rule ◽  
Clinical Decision ◽  
Central Line ◽  
Suspected Pulmonary Embolism ◽  
Clinical Predictors ◽  
Hospital System ◽  
D Dimer

BackgroundWe sought to determine clinical variables in children tested for suspected pulmonary embolism (PE) that predict PE+ outcome for the development of paediatric PE prediction rule.MethodsData were collected by query of a laboratory database for D-dimer from January 2004 to December 2014 for a large multicentre hospital system and the radiology database for pulmonary vascular imaging in children aged 5–17. Using explicit, predefined methods, trained abstractors, determined if D-dimer was sent in the evaluation of PE and then recorded predictor data which was tested for association with PE+ outcome using univariate techniques.ResultsD-dimer was ordered in 526 children for clinical suspicion of PE. Thirty-four of 526 were PE+ (6.4%, 95% CI 4.3% to 8.7%). The radiology database identified 17 additional patients with PE (n=51 PE+ total). Children evaluated for PE were primarily in the ED setting (80%), teenagers (88%) and 2:1 female:male. Children with PE had higher mean heart and higher respiratory rate and a lower pulse oximetry and haemoglobin concentration. On univariate analysis, five conditions were more frequent in PE+ compared with no PE: surgery, central line, limb immobility, prior PE or deep vein thrombosis and cancer.ConclusionsThe rate of PE diagnosis in children with D-dimer was 6.4%, similar to that seen in adults; most children with PE are over 13 years and had clinical predictors known to increase probability of PE in symptomatic adults. Future studies should use these criteria to develop a clinical decision rule for PE in children.

scholarly journals Elderly Patients in Primary Care are Still at Risks of Receiving Potentially Inappropriate Medications

2021 ◽  
Vol 12 ◽  
pp. 215013272110350
Author(s):  
Pasitpon Vatcharavongvan ◽  
Viwat Puttawanchai
Keyword(s):  
Primary Care ◽  
Primary Care Clinic ◽  
Clinical Decision ◽  
Cross Sectional Study ◽  
Study Data ◽  
Patient Specific ◽  
Potentially Inappropriate Medications ◽  
Cross Sectional ◽  
Care Clinic ◽  
Inappropriate Medications

Background Most older adults with comorbidities in primary care clinics use multiple medications and are at risk of potentially inappropriate medications (PIMs) prescription. Objective This study examined the prevalence of polypharmacy and PIMs using Thai criteria for PIMs. Methods This study was a retrospective cross-sectional study. Data were collected from electronic medical records in a primary care clinic in 2018. Samples were patients aged ≥65 years old with at least 1 prescription. Variables included age, gender, comorbidities, and medications. The list of risk drugs for Thai elderly version 2 was the criteria for PIMs. The prevalence of polypharmacy and PIMs were calculated, and multiple logistic regression was conducted to examine associations between variables and PIMs. Results Of 2806 patients, 27.5% and 43.7% used ≥5 medications and PIMs, respectively. Of 10 290 prescriptions, 47% had at least 1 PIM. The top 3 PIMs were anticholinergics, proton-pump inhibitors, and nonsteroidal anti-inflammatory drugs (NSAIDs). Polypharmacy and dyspepsia were associated with PIM prescriptions (adjusted odds ratio 2.48 [95% confident interval or 95% CI 2.07-2.96] and 3.88 [95% CI 2.65-5.68], respectively). Conclusion Prescriptions with PIMs were high in the primary care clinic. Describing unnecessary medications is crucial to prevent negative health outcomes from PIMs. Computer-based clinical decision support, pharmacy-led interventions, and patient-specific drug recommendations are promising interventions to reduce PIMs in a primary care setting.

scholarly journals Dr. Answer AI for Prostate Cancer: Predicting Biochemical Recurrence Following Radical Prostatectomy

2021 ◽  
Vol 20 ◽  
pp. 153303382110246
Author(s):  
Jihwan Park ◽  
Mi Jung Rho ◽  
Hyong Woo Moon ◽  
Jaewon Kim ◽  
Chanjung Lee ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Random Forest ◽  
Radical Prostatectomy ◽  
Prediction Model ◽  
Biochemical Recurrence ◽  
Clinical Decision ◽  
Outcome Variable ◽  
Tertiary Hospitals ◽  
Input Variables

Objectives: To develop a model to predict biochemical recurrence (BCR) after radical prostatectomy (RP), using artificial intelligence (AI) techniques. Patients and Methods: This study collected data from 7,128 patients with prostate cancer (PCa) who received RP at 3 tertiary hospitals. After preprocessing, we used the data of 6,755 cases to generate the BCR prediction model. There were 16 input variables with BCR as the outcome variable. We used a random forest to develop the model. Several sampling techniques were used to address class imbalances. Results: We achieved good performance using a random forest with synthetic minority oversampling technique (SMOTE) using Tomek links, edited nearest neighbors (ENN), and random oversampling: accuracy = 96.59%, recall = 95.49%, precision = 97.66%, F1 score = 96.59%, and ROC AUC = 98.83%. Conclusion: We developed a BCR prediction model for RP. The Dr. Answer AI project, which was developed based on our BCR prediction model, helps physicians and patients to make treatment decisions in the clinical follow-up process as a clinical decision support system.

scholarly journals Implementation of pharmacogenomic testing in oncology care (PhOCus): study protocol of a pragmatic, randomized clinical trial

2020 ◽  
Vol 12 ◽  
pp. 175883592097411
Author(s):  
Natalie Reizine ◽  
Everett E. Vokes ◽  
Ping Liu ◽  
Tien M. Truong ◽  
Rita Nanda ◽  
...  
Keyword(s):  
Clinical Laboratory ◽  
Drug Effects ◽  
Dose Intensity ◽  
Progression Free Survival ◽  
Clinical Decision ◽  
Drug Dose ◽  
Patient Specific ◽  
Drug Dosing ◽  
Patient Reported ◽  
Dose Modifications

Background: Many cancer patients who receive chemotherapy experience adverse drug effects. Pharmacogenomics (PGx) has promise to personalize chemotherapy drug dosing to maximize efficacy and safety. Fluoropyrimidines and irinotecan have well-known germline PGx associations. At our institution, we have delivered PGx clinical decision support (CDS) based on preemptively obtained genotyping results for a large number of non-oncology medications since 2012, but have not previously evaluated the utility of this strategy for patients initiating anti-cancer regimens. We hypothesize that providing oncologists with preemptive germline PGx information along with CDS will enable individualized dosing decisions and result in improved patient outcomes. Methods: Patients with oncologic malignancies for whom fluoropyrimidine and/or irinotecan-inclusive therapy is being planned will be enrolled and randomly assigned to PGx and control arms. Patients will be genotyped in a clinical laboratory across panels that include actionable variants in UGT1A1 and DPYD. For PGx arm patients, treating providers will be given access to the patient-specific PGx results with CDS prior to treatment initiation. In the control arm, genotyping will be deferred, and dosing will occur as per usual care. Co-primary endpoints are dose intensity deviation rate (the proportion of patients receiving dose modifications during the first treatment cycle), and grade ⩾3 treatment-related toxicities throughout the treatment course. Additional study endpoints will include cumulative drug dose intensity, progression-free survival, dosing of additional PGx supportive medications, and patient-reported quality of life and understanding of PGx. Discussion: Providing a platform of integrated germline PGx information may promote personalized chemotherapy dosing decisions and establish a new model of care to optimize oncology treatment planning.

scholarly journals Challenges involved in establishing a web-based clinical decision support tool in community health centers

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 100488
Author(s):  
Rachel Gold ◽  
Mary Middendorf ◽  
John Heintzman ◽  
Joan Nelson ◽  
Patrick O'Connor ◽  
...  
Keyword(s):  
Decision Support ◽  
Community Health ◽  
Clinical Decision Support ◽  
Community Health Centers ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Health Centers ◽  
Web Based ◽  
Support Tool ◽  
Clinical Decision Support Tool

scholarly journals A Framework for AI-Assisted Detection of Patent Ductus Arteriosus from Neonatal Phonocardiogram

Healthcare ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 169
Author(s):  
Sergi Gómez-Quintana ◽  
Christoph E. Schwarz ◽  
Ihor Shelevytsky ◽  
Victoriya Shelevytska ◽  
Oksana Semenova ◽  
...  
Keyword(s):  
Patent Ductus Arteriosus ◽  
Heart Defects ◽  
Ductus Arteriosus ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Heart Sounds ◽  
Decision Tree Classifier ◽  
Support Tool ◽  
Tree Classifier ◽  
Patent Ductus

The current diagnosis of Congenital Heart Disease (CHD) in neonates relies on echocardiography. Its limited availability requires alternative screening procedures to prioritise newborns awaiting ultrasound. The routine screening for CHD is performed using a multidimensional clinical examination including (but not limited to) auscultation and pulse oximetry. While auscultation might be subjective with some heart abnormalities not always audible it increases the ability to detect heart defects. This work aims at developing an objective clinical decision support tool based on machine learning (ML) to facilitate differentiation of sounds with signatures of Patent Ductus Arteriosus (PDA)/CHDs, in clinical settings. The heart sounds are pre-processed and segmented, followed by feature extraction. The features are fed into a boosted decision tree classifier to estimate the probability of PDA or CHDs. Several mechanisms to combine information from different auscultation points, as well as consecutive sound cycles, are presented. The system is evaluated on a large clinical dataset of heart sounds from 265 term and late-preterm newborns recorded within the first six days of life. The developed system reaches an area under the curve (AUC) of 78% at detecting CHD and 77% at detecting PDA. The obtained results for PDA detection compare favourably with the level of accuracy achieved by an experienced neonatologist when assessed on the same cohort.

scholarly journals Best Paper Selection

2018 ◽  
Vol 27 (01) ◽  
pp. 127-128
Keyword(s):  
Decision Support ◽  
Postoperative Delirium ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Knowledge Bases ◽  
Free Text ◽  
Health Records ◽  
Support Tool ◽  
Team Selection ◽  
Order Sets

Chen JH, Alagappan M, Goldstein MK, Asch SM, Altman RB. Decaying relevance of clinical data towards future decisions in data-driven inpatient clinical order sets. Int J Med Inform 2017 Jun;102:71-9 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28495350/ Ebadi A, Tighe PJ, Zhang L, Rashidi P. DisTeam: A decision support tool for surgical team selection. Artif Intell Med 2017 Feb;76:16-26 https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/28363285/ Fung KW, Kapusnik-Uner J, Cunningham J, Higby-Baker S, Bodenreider O. Comparison of three commercial knowledge bases for detection of drug-drug interactions in clinical decision support. J Am Med Inform Assoc 2017 Jul 1;24(4):806-12 https://academic.oup.com/jamia/article-lookup/doi/10.1093/jamia/ocx010 Mikalsen KØ, Soguero-Ruiz C, Jensen K, Hindberg K, Gran M, Revhaug A, Lindsetmo RO, Skrøvseth SO, Godtliebsen F, Jenssen R. Using anchors from free text in electronic health records to diagnose postoperative delirium. Comput Methods Programs Biomed 2017 Dec;152:105-14 https://linkinghub.elsevier.com/retrieve/pii/S0169-2607(17)31154-9

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