Predicting Clostridium difficile Toxin in Hospitalized Patients With Antibiotic-Associated Diarrhea

2007 ◽  
Vol 28 (4) ◽  
pp. 377-381 ◽  
Author(s):  
Nir Peled ◽  
Silvio Pitlik ◽  
Zmira Samra ◽  
Arkadi Kazakov ◽  
Yoram Bloch ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Positive Result ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospitalized Patients ◽  
Watery Diarrhea ◽  
Toxin A ◽  
Antibiotic Associated Diarrhea ◽  
Assay Result

Objective.Clostridium difficile infection is implicated in 20%-30% of cases of antibiotic-associated diarrhea. Studying hospitalized patients who received antibiotic therapy and developed diarrhea, our objective was to compare the clinical characteristics of patients who developed C. difficile–associated diarrhea (CDAD) with those of patients with a negative result of a stool assay for C. difficile toxin.Methods.A prospective study was done with a cohort of 217 hospitalized patients who had received antibiotics and developed diarrhea. Patients with CDAD were defined as patients who had diarrhea and a positive result for C. difficile toxin A/B by an enzyme immunoassay of stool. The variables that yielded a significant difference on univariate analysis between patients with a positive assay result and patients with a negative assay result were entered into a logistic regression model for prediction of C. difficile toxin.Setting.A 900-bed tertiary care medical center.Results.Of 217 patients, 52 (24%) had a positive result of assay for C. difficile toxin A/B in their stool. The logistic regression model included impaired functional capacity, watery diarrhea, use of a proton pump inhibitor, use of a histamine receptor blocker, leukocytosis, and hypoalbuminemia. The area under the receiver operating characteristic curve for the model as a predictor of a positive result for the stool toxin assay was 0.896 (95% confidence interval, 0.661-1.000; P<.001), with 95% specificity and 68% sensitivity.Conclusions.Our results may help clinicians to predict the risk of CDAD in hospitalized patients with antibiotic-associated diarrhea, to guide careful, specific empirical therapy, and to direct early attention to infection control issues.

Attributable Cost of Clostridium difficile Infection in Pediatric Patients

2017 ◽  
Vol 38 (12) ◽  
pp. 1472-1477 ◽  
Author(s):  
Preeti Mehrotra ◽  
Jisun Jang ◽  
Courtney Gidengil ◽  
Thomas J. Sandora
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Propensity Score ◽  
Regression Model ◽  
Generalized Linear Model ◽  
Logistic Regression Model ◽  
Propensity Scores ◽  
Healthcare Research ◽  
Design And Methods ◽  
Inpatient Database

OBJECTIVESThe attributable cost of Clostridium difficile infection (CDI) in children is unknown. We sought to determine a national estimate of attributable cost and length of stay (LOS) of CDI occurring during hospitalization in children.DESIGN AND METHODSWe analyzed discharge records of patients between 2 and 18 years of age from the Agency for Healthcare Research and Quality (AHRQ) Kids’ Inpatient Database. We created a logistic regression model to predict CDI during hospitalization based on demographic and clinical characteristics. Predicted probabilities from the logistic regression model were then used as propensity scores to match 1:2 CDI to non-CDI cases. Charges were converted to costs and compared between patients with CDI and propensity-score–matched controls. In a sensitivity analysis, we adjusted for LOS as a confounder by including it in both the propensity score and a generalized linear model predicting cost.RESULTSWe identified 8,527 pediatric hospitalizations (0.53%) with a diagnosis of CDI and 1,597,513 discharges without CDI. In our matched cohorts, the attributable cost of CDI occurring during a hospitalization ranged from $1,917 to $8,317, depending on whether model was adjusted for LOS. When not adjusting for LOS, CDI-associated hospitalizations cost 1.6 times more than non-CDI associated hospitalizations. Attributable LOS of CDI was approximately 4 days.CONCLUSIONSClostridium difficile infection in hospitalized children is associated with an economic burden similar to adult estimates. This finding supports a continued focus on preventing CDI in children as a priority. Pediatric CDI cost analyses should account for LOS as an important confounder of cost.Infect Control Hosp Epidemiol 2017;38:1472–1477

scholarly journals Variation Among Hospitals in the Continuity of Care for Older Hospitalized Patients: a Cross-sectional Cohort Study

2021 ◽  
Author(s):  
James S. Goodwin ◽  
Shuang Li ◽  
Jie Zhou ◽  
Yong-Fang Kuo ◽  
Ann Nattinger
Keyword(s):  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Model ◽  
Continuity Of Care ◽  
Logistic Regression Model ◽  
Large Degree ◽  
Patient Characteristics ◽  
Hospitalized Patients ◽  
Cross Sectional ◽  
The Impact

Abstract Background: Little is known about how continuity of care for hospitalized patients varies among hospitals. We describe the number of different general internal medicine physicians seeing hospitalized patients during a medical admission and how that varies by hospital. Methods: We conducted a retrospective study of a national 20% sample of Medicare inpatients from 01/01/16 to 12/31/18. In patients with routine medical admissions (length of stay of 3-6 days, no Intensive Care Unit stay, and seen by only one generalist per day), we assessed odds of receiving all generalist care from one generalist. We calculated rates for each hospital, adjusting for patient and hospital characteristics in a multi-level logistic regression model. Results: Among routine medical admissions with 3- to 6-day stays, only 43.1% received all their generalist care from the same physician. In those with a 3-day stay, 50.1% had one generalist providing care vs. 30.8% in those with a 6-day stay. In a two-level (admission and hospital) logistic regression model controlling for patient characteristics and length of stay, the odds of seeing just one generalist did not vary greatly by patient characteristics such as age, race/ethnicity, comorbidity or reason for admission. There were large variations in continuity of care among different hospitals and geographic areas. In the highest decile of hospitals, the adjusted mean percentage of patients receiving all generalist care from one physician was >84.1%, vs. <24.1% in the lowest decile. This large degree of variation persisted when hospitals were stratified by size, ownership, location or teaching status. Conclusions: Continuity of care provided by generalist physicians to medical inpatients varies widely among hospitals. The impact of this variation on quality of care is unknown.

scholarly journals Urine biomarkers for the prediction of mortality in COVID-19 hospitalized patients

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Daniel Morell-Garcia ◽  
David Ramos-Chavarino ◽  
Josep M. Bauça ◽  
Paula Argente del Castillo ◽  
Maria Antonieta Ballesteros-Vizoso ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Renal Involvement ◽  
Tertiary Care ◽  
Hospitalized Patients ◽  
Care Hospital ◽  
Icu Admission ◽  
Renal Tubular Cells ◽  
Prediction Of Mortality

AbstractRisk factors associated with severity and mortality attributable to COVID-19 have been reported in different cohorts, highlighting the occurrence of acute kidney injury (AKI) in 25% of them. Among other, SARS-CoV-2 targets renal tubular cells and can cause acute renal damage. The aim of the present study was to evaluate the usefulness of urinary parameters in predicting intensive care unit (ICU) admission, mortality and development of AKI in hospitalized patients with COVID-19. Retrospective observational study, in a tertiary care hospital, between March 1st and April 19th, 2020. We recruited adult patients admitted consecutively and positive for SARS-CoV-2. Urinary and serum biomarkers were correlated with clinical outcomes (AKI, ICU admission, hospital discharge and in-hospital mortality) and evaluated using a logistic regression model and ROC curves. A total of 199 COVID-19 hospitalized patients were included. In AKI, the logistic regression model with a highest area under the curve (AUC) was reached by the combination of urine blood and previous chronic kidney disease, with an AUC of 0.676 (95%CI 0.512–0.840; p = 0.023); urine specific weight, sodium and albumin in serum, with an AUC of 0.837 (95% CI 0.766–0.909; p < 0.001) for ICU admission; and age, urine blood and lactate dehydrogenase levels in serum, with an AUC of 0.923 (95%CI 0.866–0.979; p < 0.001) for mortality prediction. For hospitalized patients with COVID-19, renal involvement and early alterations of urinary and serum parameters are useful as prognostic factors of AKI, the need for ICU admission and death.

scholarly journals Variation among hospitals in the continuity of care for older hospitalized patients: a cross-sectional cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
James S. Goodwin ◽  
Shuang Li ◽  
Jie Zhou ◽  
Yong-Fang Kuo ◽  
Ann Nattinger
Keyword(s):  
Logistic Regression ◽  
Length Of Stay ◽  
Regression Model ◽  
Continuity Of Care ◽  
Logistic Regression Model ◽  
Large Degree ◽  
Patient Characteristics ◽  
Hospitalized Patients ◽  
Cross Sectional ◽  
The Impact

Abstract Background Little is known about how continuity of care for hospitalized patients varies among hospitals. We describe the number of different general internal medicine physicians seeing hospitalized patients during a medical admission and how that varies by hospital. Methods We conducted a retrospective study of a national 20% sample of Medicare inpatients from 01/01/16 to 12/31/18. In patients with routine medical admissions (length of stay of 3–6 days, no Intensive Care Unit stay, and seen by only one generalist per day), we assessed odds of receiving all generalist care from one generalist. We calculated rates for each hospital, adjusting for patient and hospital characteristics in a multi-level logistic regression model. Results Among routine medical admissions with 3- to 6-day stays, only 43.1% received all their generalist care from the same physician. In those with a 3-day stay, 50.1% had one generalist providing care vs. 30.8% in those with a 6-day stay. In a two-level (admission and hospital) logistic regression model controlling for patient characteristics and length of stay, the odds of seeing just one generalist did not vary greatly by patient characteristics such as age, race/ethnicity, comorbidity or reason for admission. There were large variations in continuity of care among different hospitals and geographic areas. In the highest decile of hospitals, the adjusted mean percentage of patients receiving all generalist care from one physician was > 84.1%, vs. < 24.1% in the lowest decile. This large degree of variation persisted when hospitals were stratified by size, ownership, location or teaching status. Conclusions Continuity of care provided by generalist physicians to medical inpatients varies widely among hospitals. The impact of this variation on quality of care is unknown.

scholarly journals Risk Factors for Mortality from COVID-19 in Hospitalized Patients: A Logistic Regression Model

2021 ◽  
Vol 21 (1) ◽  
pp. 19-27
Author(s):  
Irma Luz Yupari ◽  
Lucia Bardales Aguirre ◽  
Julio Rodriguez Azabache ◽  
Jaylin Barros Sevillano ◽  
Angela Rodríguez Díaz
Keyword(s):  
Risk Factors ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Hospitalized Patients

scholarly journals Predicting the Risk for Hospital-Onset Clostridium difficile Infection (HO-CDI) at the Time of Inpatient Admission: HO-CDI Risk Score

2015 ◽  
Vol 36 (6) ◽  
pp. 695-701 ◽  
Author(s):  
Ying P. Tabak ◽  
Richard S. Johannes ◽  
Xiaowu Sun ◽  
Carlos M. Nunez ◽  
L. Clifford McDonald
Keyword(s):  
Logistic Regression ◽  
Clostridium Difficile ◽  
Regression Model ◽  
Logistic Regression Model ◽  
Predictive Ability ◽  
Risk Identification ◽  
Inpatient Admission ◽  
Risk Scores ◽  
Retrospective Data Analysis ◽  
Time Of Admission

OBJECTIVETo predict the likelihood of hospital-onset Clostridium difficile infection (HO-CDI) based on patient clinical presentations at admissionDESIGNRetrospective data analysisSETTINGSix US acute care hospitalsPATIENTSAdult inpatientsMETHODSWe used clinical data collected at the time of admission in electronic health record (EHR) systems to develop and validate a HO-CDI predictive model. The outcome measure was HO-CDI cases identified by a nonduplicate positive C. difficile toxin assay result with stool specimens collected >48 hours after inpatient admission. We fit a logistic regression model to predict the risk of HO-CDI. We validated the model using 1,000 bootstrap simulations.RESULTSAmong 78,080 adult admissions, 323 HO-CDI cases were identified (ie, a rate of 4.1 per 1,000 admissions). The logistic regression model yielded 14 independent predictors, including hospital community onset CDI pressure, patient age ≥65, previous healthcare exposures, CDI in previous admission, admission to the intensive care unit, albumin ≤3 g/dL, creatinine >2.0 mg/dL, bands >32%, platelets ≤150 or >420 109/L, and white blood cell count >11,000 mm3. The model had a c-statistic of 0.78 (95% confidence interval [CI], 0.76–0.81) with good calibration. Among 79% of patients with risk scores of 0–7, 19 HO-CDIs occurred per 10,000 admissions; for patients with risk scores >20, 623 HO-CDIs occurred per 10,000 admissions (P<.0001).CONCLUSIONUsing clinical parameters available at the time of admission, this HO-CDI model demonstrated good predictive ability, and it may have utility as an early risk identification tool for HO-CDI preventive interventions and outcome comparisons.Infect Control Hosp Epidemiol 2015;00(0):1–7

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