Dataset Information

Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection.

ABSTRACT:

Background

Clostridium difficile infection poses a significant healthcare burden. However, the derivation of a simple, evidence based prediction rule to assist patient management has not yet been described.

Method

Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients. The resulting prediction rule was validated on independent data from a cohort of 158 patients described by Bhangu et al. (Colorectal Disease, 12(3):241-246, 2010).

Results

Serum albumin levels (g/L) (P = 0.001), respiratory rate (resps /min) (P = 0.002), C-reactive protein (mg/L) (P = 0.034) and white cell count (mcL) (P = 0.049) were predictors of all-cause mortality. Threshold levels of serum albumin ≤ 24.5 g/L, C- reactive protein >228 mg/L, respiratory rate >17 resps/min and white cell count >12 × 10(3) mcL were associated with an increased risk of all-cause mortality. A simple four variable prediction rule was devised based on these threshold levels and when tested on the initial data, yield an area under the curve score of 0.754 (P < 0.001) using receiver operating characteristics. The prediction rule was then evaluated using independent data, and yield an area under the curve score of 0.653 (P = 0.001).

Conclusions

Four easily measurable clinical variables can be used to assess the risk of mortality of patients with Clostridium difficile infection and remains robust with respect to independent data.

SUBMITTER: Butt E

PROVIDER: S-EPMC3733667 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection.

Butt Emma E Foster Jane A H JA Keedwell Edward E Bell Julia E A JE Titball Richard W RW Bhangu Aneel A Michell Stephen L SL Sheridan Ray R

BMC infectious diseases 20130712

<h4>Background</h4>Clostridium difficile infection poses a significant healthcare burden. However, the derivation of a simple, evidence based prediction rule to assist patient management has not yet been described.<h4>Method</h4>Univariate, multivariate and decision tree procedures were used to deduce a prediction rule from over 186 variables; retrospectively collated from clinical data for 213 patients. The resulting prediction rule was validated on independent data from a cohort of 158 patient ...[more]

PMID: 23849267

Similar Datasets

Project description:BackgroundRespiratory viruses are important etiologies of community-acquired pneumonia. However, current knowledge on the prognosis of respiratory virus-related pneumonia (RV-p) is limited. Thus, here we aimed to establish a clinical predictive model for mortality of patients with RV-p.MethodsA total of 1431 laboratory-confirmed patients with RV-p, including 1169 and 262 patients from respective derivation and validation cohorts from five teaching hospitals in China were assessed between January 2010 and December 2019. A prediction rule was established on the basis of risk factors for 30-day mortality of patients with RV-p from the derivation cohort using a multivariate logistic regression model.ResultsThe 30-day mortality of patients with RV-p was 16.8% (241/1431). The RV-p score was composed of nine predictors (including respective points of mortality risk): (a) age ⩾65 years (1 point); (b) chronic obstructive pulmonary disease (1 point); (c) mental confusion (1 point); (d) blood urea nitrogen (1 point); (e) cardiovascular disease (2 points); (f) smoking history (2 points); (g) arterial pressure of oxygen/fraction of inspiration oxygen (PaO2/FiO2) < 250 mmHg (2 points); (h) lymphocyte counts <0.8 × 109/L (2 points); (i) arterial PH < 7.35 (3 points). A total of six points was used as the cut-off value for mortality risk stratification. Our model showed a sensitivity of 0.831 and a specificity of 0.783. The area under the receiver operating characteristic curve was more prominent for RV-p scoring [0.867, 95% confidence interval (CI)0.846-0.886] when compared with both pneumonia severity index risk (0.595, 95% CI 0.566-0.624, p < 0.001) and CURB-65 scoring (0.739, 95% CI 0.713-0.765, p < 0.001).ConclusionRV-p scoring was able to provide a good predictive accuracy for 30-day mortality, which accounted for a more effective stratification of patients with RV-p into relevant risk categories and, consequently, help physicians to make more rational clinical decisions.The reviews of this paper are available via the supplemental material section.

Project description:ImportanceRecent evidence from an animal model suggests that peripheral loss of eosinophils in Clostridium difficile infection (CDI) is associated with severe disease. The ability to identify high-risk patients with CDI as early as the time of admission could improve outcomes by guiding management decisions.ObjectiveTo construct a model using clinical indices readily available at the time of hospital admission, including peripheral eosinophil counts, to predict inpatient mortality in patients with CDI.Design, setting, and participantsIn a cohort study, a total of 2065 patients admitted for CDI through the emergency department of 2 tertiary referral centers from January 1, 2005, to December 31, 2015, formed a training and a validation cohort. The sample was stratified by admission eosinophil count (0.0 cells/μL or >0.0 cells/μL), and multivariable logistic regression was used to construct a predictive model for inpatient mortality as well as other disease-related outcomes.Main outcomes and measuresInpatient mortality was the primary outcome. Secondary outcomes included the need for a monitored care setting, need for vasopressors, and rates of inpatient colectomy.ResultsOf the 2065 patients in the study, 1092 (52.9%) were women and patients had a mean (SD) age of 63.4 (18.4) years. Those with an undetectable eosinophil count at admission had increased in-hospital mortality in both the training (odds ratio [OR], 2.01; 95% CI, 1.08-3.73; P = .03) and validation (OR, 2.26; 95% CI, 1.33-3.83; P = .002) cohorts in both univariable and multivariable analysis. Undetectable eosinophil counts were also associated with indicators of severe sepsis, such as admission to monitored care settings (OR, 1.40; 95% CI, 1.06-1.86), the need for vasopressors (OR, 2.08; 95% CI, 1.32-3.28), and emergency total colectomy (OR, 2.56; 95% CI, 1.12-5.87). Other significant predictors of mortality at admission included increasing comorbidity burden (for each 1-unit increase: OR, 1.13; 95% CI, 1.05-1.22) and lower systolic blood pressures (for each 1-mm Hg increase: OR, 0.99; 95% CI, 0.98-1.00). In a subgroup analysis of patients presenting without initial tachycardia or hypotension, only patients with undetectable admission eosinophil counts, but not those with an elevated white blood cell count, had significantly increased odds of inpatient mortality (OR, 5.76; 95% CI, 1.99-16.64).Conclusions and relevanceThis study describes a simple, widely available, inexpensive model predicting CDI severity and mortality to identify at-risk patients at the time of admission.

Project description:ImportanceTraumatic cervical spinal cord injury (SCI) can result in debilitating paralysis. Following cervical SCI, accurate early prediction of upper limb recovery can serve an important role in guiding the appropriateness and timing of reconstructive therapies.ObjectiveTo develop a clinical prediction rule to prognosticate upper limb functional recovery after cervical SCI.Design, setting, and participantsThis prognostic study was a retrospective review of a longitudinal cohort study including patients enrolled in the National SCI model systems (SCIMS) database in US. Eligible patients were 15 years or older with tetraplegia (neurological level of injury C1-C8, American Spinal Cord Injury Association [ASIA] impairment scale [AIS] A-D), with early (within 1 month of SCI) and late (1-year follow-up) clinical examinations from 2011 to 2016. The data analysis was conducted from September 2021 to June 2022.Main outcomes and measuresThe primary outcome was a composite of dependency in eating, bladder management, transfers, and locomotion domains of functional independence measure at 1-year follow-up. Each domain ranges from 1 to 7 with a lower score indicating greater functional dependence. Composite dependency was defined as a score of 4 or higher in at least 3 chosen domains. Multivariable logistic regression was used to predict the outcome based on early neurological variables. Discrimination was quantified using C statistics, and model performance was internally validated with bootstrapping and 10-fold cross-validation. The performance of the prediction score was compared with AIS grading. Data were split into derivation (2011-2014) and temporal-validation (2015-2016) cohorts.ResultsAmong 2373 patients with traumatic cervical SCI, 940 had complete 1-year outcome data (237 patients [25%] aged 60 years or older; 753 men [80%]). The primary outcome was present in 118 patients (13%), which included 92 men (78%), 83 (70%) patients who were younger than 60 years, and 73 (62%) patients experiencing AIS grade A SCI. The variables significantly associated with the outcome were age (age 60 years or older: OR, 2.31; 95% CI, 1.26-4.19), sex (men: OR, 0.60; 95% CI, 0.31-1.17), light-touch sensation at C5 (OR, 0.44; 95% CI, 0.44-1.01) and C8 (OR, 036; 95% CI, 0.24-0.53) dermatomes, and motor scores of the elbow flexors (C5) (OR, 0.74; 95% CI, 0.60-0.89) and wrist extensors (C6) (OR, 0.61; 95% CI, 0.49-0.75). A multivariable model including these variables had excellent discrimination in distinguishing dependent from independent patients in the temporal-validation cohort (C statistic, 0.90; 95% CI, 0.88-0.93). A clinical prediction score (range, 0 to 45 points) was developed based on these measures, with higher scores increasing the probability of dependency. The discrimination of the prediction score was significantly higher than from AIS grading (change in AUC, 0.14; 95% CI, 0.10-0.18; P < .001).Conclusions and relevanceThe findings of this study suggest that this prediction rule may help prognosticate upper limb function following cervical SCI. This tool can be used to set patient expectations, rehabilitation goals, and aid decision-making regarding the appropriateness and timing for upper limb reconstructive surgeries.

Project description:To develop and validate a simple clinical prediction rule, based on variables easily measurable at admission, to identify patients at high risk of developing delirium during their hospital stay on an internal medicine ward. Prospective study of two cohorts of patients admitted between 1 May and 30 June 2008 (derivation cohort), and between 1 May and 30 June 2009 (validation cohort). A tertiary hospital in Donostia-Gipuzkoa (Spain). In total 397 patients participated in the study. The mean age and incidence of delirium were 75.9 years and 13%, respectively, in the derivation cohort, and 75.8 years and 25% in the validation cohort. The predictive variables analysed and finally included in the rule were: being aged 85 years old or older, being dependent in five or more activities of daily living, and taking two or more psychotropic drugs (antipsychotics, benzodiazepines, antidepressants, anticonvulsant and/or antidementia drugs). The variable of interest was delirium as defined by the short Confusion Assessment Method, which assesses four characteristics: acute onset and fluctuating course, inattention, disorganised thinking and altered level of consciousness. We developed a rule in which the individual risk of delirium is obtained by adding one point for each criterion met (age≥85, high level of dependence, and being on psychotropic medication). The result is considered positive if the score is ≥1. The rule accuracy was: sensitivity=93.4% (95% CI 85.5% to 97.2%), specificity=60.6% (95% CI 54.1% to 66.8%), positive predictive value=44.4% (95% CI 36.9% to 52.1%) and negative predictive value=96.5% (95% CI: 92% to 98.5%). The area under the receiver operator characteristic (ROC) curve was 0.85 for the validation cohort. The presence or absence of any of the three predictive factors (age≥85, high level of dependence and psychotropic medication) allowed us to classify patients on internal medicine wards according to the risk of developing delirium. The simplicity of the variables in our clinical prediction rule means that the data collection required is feasible in busy medicine units.

Dataset Information

Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection.

Background

Method

Results

Conclusions

Publications

Derivation and validation of a simple, accurate and robust prediction rule for risk of mortality in patients with Clostridium difficile infection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets