Matthew Ronan, MD

Clinical question: Can a delirium severity score that coincides with clinical outcomes be developed?

Background: Quantification of the severity of delirium is important. Building on the Confusion Assessment Method (CAM) scoring system, the CAM-S was developed to help evaluate the severity of delirium.

Study design: Validation analysis in two independent cohorts.

Setting: Three academic medical centers.

Synopsis: Two cohorts of patients were included. The sample from the SAGES [Successful Aging after Elective Surgery] study included 300 patients aged 70 years or older undergoing major surgery. The sample from the Project Recovery study included 919 medical patients aged 70 years or older. There was a predominance of women in both studies. Patients were excluded if they had delirium or dementia present at baseline, recent hospitalization, a terminal diagnosis, vision and auditory impairment, or inability to speak English. A short, four-item form and 10-item longer form were developed, and scores on forms were correlated with outcomes both in the hospital and after discharge.

A higher severity score in both short and long forms was associated with a higher length of stay, as well as relative risk of death and nursing home residence at 90 days.

Limitations to the study were that all patients in the study were 70 years and older and that the Project Recovery sample used an older data set from 1995-1998.

Bottom line: The CAM-S represents a new delirium severity measurement system; it is associated with several clinical outcomes.

Citation: Inouye SK, Kosar CM, Tommet D, et al. The CAM-S: development and validation of a new scoring system for delirium severity in 2 cohorts. Ann Intern Med. 2014;160(8):526-533.

Clinical question: Can a delirium severity score that coincides with clinical outcomes be developed?

Background: Quantification of the severity of delirium is important. Building on the Confusion Assessment Method (CAM) scoring system, the CAM-S was developed to help evaluate the severity of delirium.

Study design: Validation analysis in two independent cohorts.

Setting: Three academic medical centers.

Synopsis: Two cohorts of patients were included. The sample from the SAGES [Successful Aging after Elective Surgery] study included 300 patients aged 70 years or older undergoing major surgery. The sample from the Project Recovery study included 919 medical patients aged 70 years or older. There was a predominance of women in both studies. Patients were excluded if they had delirium or dementia present at baseline, recent hospitalization, a terminal diagnosis, vision and auditory impairment, or inability to speak English. A short, four-item form and 10-item longer form were developed, and scores on forms were correlated with outcomes both in the hospital and after discharge.

A higher severity score in both short and long forms was associated with a higher length of stay, as well as relative risk of death and nursing home residence at 90 days.

Limitations to the study were that all patients in the study were 70 years and older and that the Project Recovery sample used an older data set from 1995-1998.

Bottom line: The CAM-S represents a new delirium severity measurement system; it is associated with several clinical outcomes.

Citation: Inouye SK, Kosar CM, Tommet D, et al. The CAM-S: development and validation of a new scoring system for delirium severity in 2 cohorts. Ann Intern Med. 2014;160(8):526-533.

Clinical question: Can a delirium severity score that coincides with clinical outcomes be developed?

Background: Quantification of the severity of delirium is important. Building on the Confusion Assessment Method (CAM) scoring system, the CAM-S was developed to help evaluate the severity of delirium.

Study design: Validation analysis in two independent cohorts.

Setting: Three academic medical centers.

Synopsis: Two cohorts of patients were included. The sample from the SAGES [Successful Aging after Elective Surgery] study included 300 patients aged 70 years or older undergoing major surgery. The sample from the Project Recovery study included 919 medical patients aged 70 years or older. There was a predominance of women in both studies. Patients were excluded if they had delirium or dementia present at baseline, recent hospitalization, a terminal diagnosis, vision and auditory impairment, or inability to speak English. A short, four-item form and 10-item longer form were developed, and scores on forms were correlated with outcomes both in the hospital and after discharge.

A higher severity score in both short and long forms was associated with a higher length of stay, as well as relative risk of death and nursing home residence at 90 days.

Limitations to the study were that all patients in the study were 70 years and older and that the Project Recovery sample used an older data set from 1995-1998.

Bottom line: The CAM-S represents a new delirium severity measurement system; it is associated with several clinical outcomes.

Citation: Inouye SK, Kosar CM, Tommet D, et al. The CAM-S: development and validation of a new scoring system for delirium severity in 2 cohorts. Ann Intern Med. 2014;160(8):526-533.

Clinical question: What are the risk factors identified at the onset of illness that are associated with recurrent C. diff infection?

Background: After initial infection, 10%-25% of patients experience recurrent C. diff infection (CDI). The identification of patients at high risk of recurrence would be beneficial for therapeutic decision making.

Study design: Retrospective cohort study.

Setting: Large, urban, academic medical center.

Synopsis: Authors included 4,196 patients with an initial infection, defined by a positive C. diff toxin assay and unformed stools. A repeat positive toxin within 42 days of completing treatment for the initial infection represented recurrent CDI. Multiple characteristics were examined to identify risks of recurrent infection, including demographics and those related to acute and chronic disease. A logistic regression model was used to identify risk factors for recurrence.

Four hundred twenty-five patients (10.1%) had recurrent infection. Age, fluoroquinolone and high-risk antibiotic use, community-acquired healthcare-associated infection, multiple hospitalizations, and gastric acid suppression were found to predict recurrent infection through multivariate analysis.

Limitations of the study included potential confounding, use of observational data, and generalizability, given the urban academic medical center setting. This prediction model differs from previously developed models in that it identifies factors present at the onset of infection.

Bottom line: Multiple factors identified at the onset of illness can predict CDI recurrence.

Citation: Zilberberg MD, Reske K, Olsen M, Yan Y, Dubberke ER. Development and validation of a recurrent Clostridium difficile risk-prediction model. J Hosp Med. 2014;9(7):418-423.

Clinical question: What are the risk factors identified at the onset of illness that are associated with recurrent C. diff infection?

Background: After initial infection, 10%-25% of patients experience recurrent C. diff infection (CDI). The identification of patients at high risk of recurrence would be beneficial for therapeutic decision making.

Study design: Retrospective cohort study.

Setting: Large, urban, academic medical center.

Synopsis: Authors included 4,196 patients with an initial infection, defined by a positive C. diff toxin assay and unformed stools. A repeat positive toxin within 42 days of completing treatment for the initial infection represented recurrent CDI. Multiple characteristics were examined to identify risks of recurrent infection, including demographics and those related to acute and chronic disease. A logistic regression model was used to identify risk factors for recurrence.

Four hundred twenty-five patients (10.1%) had recurrent infection. Age, fluoroquinolone and high-risk antibiotic use, community-acquired healthcare-associated infection, multiple hospitalizations, and gastric acid suppression were found to predict recurrent infection through multivariate analysis.

Limitations of the study included potential confounding, use of observational data, and generalizability, given the urban academic medical center setting. This prediction model differs from previously developed models in that it identifies factors present at the onset of infection.

Bottom line: Multiple factors identified at the onset of illness can predict CDI recurrence.

Citation: Zilberberg MD, Reske K, Olsen M, Yan Y, Dubberke ER. Development and validation of a recurrent Clostridium difficile risk-prediction model. J Hosp Med. 2014;9(7):418-423.

Clinical question: What are the risk factors identified at the onset of illness that are associated with recurrent C. diff infection?

Background: After initial infection, 10%-25% of patients experience recurrent C. diff infection (CDI). The identification of patients at high risk of recurrence would be beneficial for therapeutic decision making.

Study design: Retrospective cohort study.

Setting: Large, urban, academic medical center.

Synopsis: Authors included 4,196 patients with an initial infection, defined by a positive C. diff toxin assay and unformed stools. A repeat positive toxin within 42 days of completing treatment for the initial infection represented recurrent CDI. Multiple characteristics were examined to identify risks of recurrent infection, including demographics and those related to acute and chronic disease. A logistic regression model was used to identify risk factors for recurrence.

Four hundred twenty-five patients (10.1%) had recurrent infection. Age, fluoroquinolone and high-risk antibiotic use, community-acquired healthcare-associated infection, multiple hospitalizations, and gastric acid suppression were found to predict recurrent infection through multivariate analysis.

Limitations of the study included potential confounding, use of observational data, and generalizability, given the urban academic medical center setting. This prediction model differs from previously developed models in that it identifies factors present at the onset of infection.

Bottom line: Multiple factors identified at the onset of illness can predict CDI recurrence.

Citation: Zilberberg MD, Reske K, Olsen M, Yan Y, Dubberke ER. Development and validation of a recurrent Clostridium difficile risk-prediction model. J Hosp Med. 2014;9(7):418-423.