A prediction model for <i>Clostridium difficile</i> recurrence

LaBarbera, Francis D.; Nikiforov, Ivan; Parvathenani, Arvin; Pramil, Varsha; Gorrepati, Venkata Subhash

doi:10.3402/jchimp.v5.26033

Cited by 27 publications

(34 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Therefore, it seems that preventing any recurrence is the key objective, which should be an imperative for all CDI care. Though there are published analyses reporting risk factors for any CDI recurrence, the risk factors for multiple rCDI vs. 1 rCDI have not been extensively studied before 8,[18][19][20][21] .…”

Section: Discussionmentioning

confidence: 99%

Healthcare resource utilization and direct medical costs associated with index and recurrent Clostridioides difficile infection: a real-world data analysis

Feuerstadt

Stong

Dahdal

et al. 2020

Journal of Medical Economics

View full text Add to dashboard Cite

Aims: This study aimed to evaluate all-cause economic outcomes, healthcare resource utilization (HRU), and costs in patients with Clostridioides difficile infection (CDI) and recurrent CDI (rCDI) using commercial claims from a large database representing various healthcare settings. Materials and methods: A retrospective analysis of commercial claims data from the IQVIA PharMetrics Plus database was conducted for patients aged 18-64 years with CDI episodes requiring inpatient stay with CDI diagnosis code or an outpatient medical claim for CDI plus a CDI treatment. Index CDI episodes occurred between 1 January 2010 and 30 June 2017, including only those where patients were observable 6 months before and 12 months after the index episode. Each CDI episode was followed by a 14-d claim-free period. rCDI was defined as another CDI episode within an 8-week window following the claim-free period. HRU, all-cause direct medical costs and time to rCDI were calculated over 12 months and stratified by number of rCDI episodes. Results: A total of 46,571 patients with index CDI were included. Mean time from one CDI episode to the next was approximately 1 month. In the 12-month follow-up period, those with no recurrence had 1.4 inpatient visits per person and those with 3 or more recurrences had 5.8. Most patients with 3 or more recurrences had 2 or more hospital admissions. The mean annual, total all-cause direct medical costs per patient were $71,980 for those with no recurrence and $207,733 for those with 3 or more recurrences. Limitations: The study included individuals 18-64 years only. A stringent definition of rCDI was used, which may have underestimated the incidence of rCDI. Conclusions: CDI and rCDI are associated with substantial healthcare resource utilization and direct medical costs. Timing of recurrences can be predictable, providing a window of opportunity for interventions. Prevention of multiple rCDI appears essential to reduce healthcare costs.

show abstract

Section: Discussionmentioning

confidence: 99%

Healthcare resource utilization and direct medical costs associated with index and recurrent Clostridioides difficile infection: a real-world data analysis

Feuerstadt

Stong

Dahdal

et al. 2020

Journal of Medical Economics

View full text Add to dashboard Cite

show abstract

“…In total, 21 studies were retrospective 33,35,70-88 and six were prospective [89][90][91][92][93][94] . There were 21 single-center studies 33,70-75, [77][78][79][80][81][82][83][86][87][88][90][91][92]94 and six multi-center studies 35,76,84,85,89,93 . Seven studies were time series 71,78,82,[84][85][86]92 , 18 studies were case series 33,35,70, [72][73][74][75][76]80,81,83,[87][88][89][90][91]93,94 , one was a case-control 77 and one was a matched-controlled study …”

Section: Study Characteristicsmentioning

confidence: 99%

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Medic

Atallah

et al. 2019

F1000Res

View full text Add to dashboard Cite

Background: Clinical decision support (CDS) systems have emerged as tools providing intelligent decision making to address challenges of critical care. CDS systems can be based on existing guidelines or best practices; and can also utilize machine learning to provide a diagnosis, recommendation, or therapy course. Methods: This research aimed to identify evidence-based study designs and outcome measures to determine the clinical effectiveness of clinical decision support systems in the detection and prediction of hemodynamic instability, respiratory distress, and infection within critical care settings. PubMed, ClinicalTrials.gov and Cochrane Database of Systematic Reviews were systematically searched to identify primary research published in English between 2013 and 2018. Studies conducted in the USA, Canada, UK, Germany and France with more than 10 participants per arm were included. Results: In studies on hemodynamic instability, the prediction and management of septic shock were the most researched topics followed by the early prediction of heart failure. For respiratory distress, the most popular topics were pneumonia detection and prediction followed by pulmonary embolisms. Given the importance of imaging and clinical notes, this area combined Machine Learning with image analysis and natural language processing. In studies on infection, the most researched areas were the detection, prediction, and management of sepsis, surgical site infections, as well as acute kidney injury. Overall, a variety of Machine Learning algorithms were utilized frequently, particularly support vector machines, boosting techniques, random forest classifiers and neural networks. Sensitivity, specificity, and ROC AUC were the most frequently reported performance measures. Conclusion: This review showed an increasing use of Machine Learning for CDS in all three areas. Large datasets are required for training these algorithms; making it imperative to appropriately address, challenges such as class imbalance, correct labelling of data and missing data. Recommendations are formulated for the development and successful adoption of CDS systems.

show abstract

“…6,[8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] Most of these studies target patients admitted to hospitals or living in long-term healthcare facilities. [9][10][11][17][18][19][22][23][24][26][27][28] or C. difficile infection cases risking recurrence. 13,19,21,25 The present study adopts a novel approach by targeting a general, unselected population of adults in the primary care setting coupled with secondary care data for the same population to aim for a comprehensive detection of cases of C. difficile infection.…”

Section: Introductionmentioning

confidence: 99%

“…[9][10][11][17][18][19][22][23][24][26][27][28] or C. difficile infection cases risking recurrence. 13,19,21,25 The present study adopts a novel approach by targeting a general, unselected population of adults in the primary care setting coupled with secondary care data for the same population to aim for a comprehensive detection of cases of C. difficile infection.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a risk score to predict future Clostridium difficile disease using UK primary care and hospital data in Clinical Practice Research Datalink

Marley¹,

Hahi

Ferreira³

et al. 2019

Human Vaccines & Immunotherapeutics

View full text Add to dashboard Cite

We evaluated the applicability of a Clostridium difficile infection (CDI) risk index developed for patients at hospital discharge to identify persons at high-risk of CDI in a primary care population. This retrospective observational study used data from the UK Clinical Practice Research Datalink, linked with Hospital Episodes Statistics. The risk index was based on the following patient characteristics: age, previous hospitalizations, days in hospital, and prior antibiotics use. Individual risk scores were calculated by summing points assigned to pre-defined categories for each characteristic. We assessed the association of risk factors with CDI by multivariate logistic regression. The estimated CDI incidence rate was 4/10,000 and 2/10,000 person-years in 2008 and 2012, respectively. On an index with a maximal risk of 19, a cut-off for high risk of ≥7 had sensitivity, specificity and positive predictive values of 80%, 87% and 12%, respectively. A high-risk person had a ~ 35% higher risk of CDI than a low-risk person. Multivariate risk factor analysis indicated a need to reconsider the relative risk scores. The CDI risk index can be applied to the UK primary care population and help identify study populations for vaccine development studies. Reassessing the relative weights assigned to risk factors could improve the index performance in this setting.

show abstract

A prediction model for Clostridium difficile recurrence

Cited by 27 publications

References 17 publications

Healthcare resource utilization and direct medical costs associated with index and recurrent Clostridioides difficile infection: a real-world data analysis

Healthcare resource utilization and direct medical costs associated with index and recurrent Clostridioides difficile infection: a real-world data analysis

Evidence-based Clinical Decision Support Systems for the prediction and detection of three disease states in critical care: A systematic literature review

Evaluation of a risk score to predict future Clostridium difficile disease using UK primary care and hospital data in Clinical Practice Research Datalink

Contact Info

Product

Resources

About