Dataset Information

CURB-65 pneumonia severity assessment adapted for electronic decision support.

ABSTRACT:

Background

Accurate severity assessment is crucial to the initial management of community-acquired pneumonia (CAP). The CURB-65 (confusion, uremia, respiratory rate, BP, age ? 65 years) score contains data that are entered routinely in electronic medical records and are, thus, electronically calculable. The aim of this study was to determine whether an electronically generated severity estimate using CURB-65 elements as continuous and weighted variables better predicts 30-day mortality than the traditional CURB-65.

Methods

In a retrospective cohort study at a US university-affiliated community teaching hospital, we identified 2,069 patients aged 18 years or older with CAP confirmed by radiographic findings in the ED. CURB-65 elements were extracted from the electronic medical record, and 30-day mortality was identified with the Utah Population Database. Performance of a severity prediction model using continuous and weighted CURB-65 variables was compared with the traditional CURB-65 in the US derivation population and validated in the original 1,048 patients from the CURB-65 international derivation study.

Results

The traditional, binary CURB-65 score predicted mortality in the US cohort with an area under the curve (AUC) of 0.82. Our severity prediction model generated from continuous, weighted CURB-65 elements was superior to the traditional CURB-65, with an out-of-bag AUC of 0.86 (P < .001). This finding was validated in the international database, with an AUC of 0.85 for the electronic model compared with 0.80 for the traditional CURB-65 (P = .01).

Conclusions

Using CURB-65 elements as continuous and weighted data improved prediction of 30-day mortality and could be used as a real-time, electronic decision support tool or to adjust outcomes by severity when comparing processes of care.

SUBMITTER: Jones BE

PROVIDER: S-EPMC3198487 | biostudies-literature | 2011 Jul

REPOSITORIES: biostudies-literature

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Publications

CURB-65 pneumonia severity assessment adapted for electronic decision support.

Jones Barbara E BE Jones Jason J Bewick Thomas T Lim Wei Shen WS Aronsky Dominik D Brown Samuel M SM Boersma Wim G WG van der Eerden Menno M MM Dean Nathan C NC

Chest 20101216 1

<h4>Background</h4>Accurate severity assessment is crucial to the initial management of community-acquired pneumonia (CAP). The CURB-65 (confusion, uremia, respiratory rate, BP, age ≥ 65 years) score contains data that are entered routinely in electronic medical records and are, thus, electronically calculable. The aim of this study was to determine whether an electronically generated severity estimate using CURB-65 elements as continuous and weighted variables better predicts 30-day mortality t ...[more]

PMID: 21163875

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Project description:BackgroundThe Confusion, Urea > 7 mM, Respiratory Rate ≥ 30 breaths/min, BP < 90 mm Hg (Systolic) or < 60 mm Hg (Diastolic), Age ≥ 65 Years (CURB-65) score and the Pneumonia Severity Index (PSI) are well-established clinical prediction rules for predicting mortality in patients hospitalized with community-acquired pneumonia (CAP). SARS-CoV-2 has emerged as a new etiologic agent for CAP, but the role of CURB-65 score and PSI have not been established.Research questionHow effective are CURB-65 score and PSI at predicting in-hospital mortality resulting from SARS-CoV-2 CAP compared with non-SARS-CoV-2 CAP? Can these clinical prediction rules be optimized to predict mortality in SARS-CoV-2 CAP by addition of procalcitonin and D-dimer?Study design and methodsSecondary analysis of two prospective cohorts of patients with SARS-CoV-2 CAP or non-SARS-CoV-2 CAP from eight adult hospitals in Louisville, Kentucky.ResultsThe in-hospital mortality rate was 19% for patients with SARS-CoV-2 CAP and 6.5% for patients with non-SARS-CoV-2 CAP. For the PSI score, receiver operating characteristic (ROC) curve analysis resulted in an area under the ROC curve (AUC) of 0.82 (95% CI, 0.78-0.86) and 0.79 (95% CI, 0.77-0.80) for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP, respectively. For the CURB-65 score, ROC analysis resulted in an AUC of 0.79 (95% CI, 0.75-0.84) and 0.75 (95% CI, 0.73-0.77) for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP, respectively. In SARS-CoV-2 CAP, the addition of D-dimer (optimal cutoff, 1,813 μg/mL) and procalcitonin (optimal cutoff, 0.19 ng/mL) to PSI and CURB-65 score provided negligible improvement in prognostic performance.InterpretationPSI and CURB-65 score can predict in-hospital mortality for patients with SARS-CoV-2 CAP and non-SARS-CoV-2 CAP comparatively. In patients with SARS-CoV-2 CAP, the inclusion of either D-dimer or procalcitonin to PSI or CURB-65 score did not improve the prognostic performance of either score. In patients with CAP, regardless of cause, PSI and CURB-65 score remain adequate for predicting mortality in clinical practice.

Project description:BackgroundMinistries of health in low- and middle-income countries often lack timely quality data for data-driven decision making in healthcare networks. We describe the design and implementation of decision-support electronic tools by the Ministry of Health of the State of Chiapas, in Mexico, as part of Salud Mesoamerica Initiative.MethodsThree electronic decision-support tools were designed through an iterative process focused on streamlined implementation: 1) to collect and report health facility data at health facilities; 2) to compile and analyze data at health district and central level; and, 3) to support stratified sampling of health facilities. Data was collected for five composite indicators measuring availability of equipment, medicines, and supplies for maternal and child health. Quality Assurance Teams collected data, evaluated results and supported quality improvement. Data was also analyzed at the central level and health districts for decision-making.ResultsData from 300 health facilities in four health districts was collected and analyzed (November 2014-June 2015). The first wave revealed gaps on availability of equipment and supplies in more than half of health facilities. Electronic tools provided the ministry of health officers new ways to visualize data, identify patterns and make hypothesis on root-causes. Between the first and second measurement, the number of missing items decreased, and actions performed by quality improvement teams became more proactive. In the final measurement, 89.7-100% of all health facilities achieved all the required items for each indicator.ConclusionsOur experience could help guide others seeking to implement electronic decision-support tools in low- and middle-income countries. Electronic decision-support tools supported data-driven decision-making by identifying gaps on heatmaps and graphs at the health facility, subdistrict, district or state level. Through a rapid improvement process, the Ministry of Health met targets of externally verified indicators. Using available information technology resources facilitated prompt implementation and adoption of technology.

Project description:BackgroundElectronic health record (EHR)-based clinical decision support tools can improve the use of evidence-based clinical guidelines for preeclampsia management that can reduce maternal mortality and morbidity. No study has investigated the organizational capabilities that enable hospitals to use EHR-based decision support tools to manage preeclampsia.ObjectiveTo examine the association of organizational capabilities and hospital adoption of EHR-based decision support tools for preeclampsia management.MethodsCross-sectional analyses of hospitals providing obstetric care in 2017. In total, 739 hospitals responded to the 2017-2018 National Survey of Healthcare Organizations and Systems (NSHOS) and were linked to the 2017 American Hospital Association (AHA) Annual Survey Database and the Area Health Resources File (AHRF). A total of 425 hospitals providing obstetric care across 49 states were included in the analysis. The main outcome was whether a hospital adopted EHR-based clinical decision support tools for preeclampsia management. Hospital organizational capabilities assessed as predictors include EHR functions, adoption of evidence-based clinical treatments, use of quality improvement methods, and dissemination processes to share best patient care practices. Logistic regression estimated the association of hospital organizational capabilities and hospital adoption of EHR-based decision support tools to manage preeclampsia, controlling for hospital structural and patient sociodemographic characteristics.ResultsTwo-thirds of the hospitals (68%) adopted EHR-based decision support tools for preeclampsia, and slightly more than half (56%) of hospitals had a single EHR system. Multivariable regression results indicate that hospitals with a single EHR system were more likely to adopt EHR-based decision support tools for preeclampsia (17.4 percentage points; 95% CI, 1.9 to 33.0; P < .05) than hospitals with a mixture of EHR and paper-based systems. Compared with hospitals having multiple EHRs, on average, hospitals having a single EHR were also more likely to adopt the tools by 9.3 percentage points, but the difference was not statistically significant (95% CI, -1.3 to 19.9). Hospitals with more processes to aid dissemination of best patient care practices were also more likely to adopt EHR-based decision-support tools for preeclampsia (0.4 percentage points; 95% CI, 0.1 to 0.6, for every 1-unit increase in dissemination processes; P < .01).ConclusionStandardized EHRs and policies to disseminate evidence are foundational hospital capabilities that can help advance the use of EHR-based decision support tools for preeclampsia management in the approximately one-third of US hospitals that still do not use them.

Dataset Information

CURB-65 pneumonia severity assessment adapted for electronic decision support.

Background

Methods

Results

Conclusions

Publications

CURB-65 pneumonia severity assessment adapted for electronic decision support.

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