Title: CLINICAL AND HEALTH CARE UTILIZATION VARIABLES CAN PREDICT 90-
DAY HOSPITAL RE-ADMISSION IN ADULTS WITH CROHN’S DISEASE FOR POINT
OF CARE RISK EVALUATION
Version: 0 Date: 09 Dec 2023
Reviewer's report:
Dziegielewski et al. performed a retrospective study of people with CD admitted to Ottawa
Hospital between 2009–2016 will the goal of developing a multivariable risk prediction model
for 90-day hospital re-admission.
Overall, this article is very well-structured, identified a clear clinical gap with focused aims
to address the gap, detailed and appropriate methodology, and informative (and potentially
actionable) results. The study identified several factors associated with re-admission risk
including prior CD-related hospitalizations, new diagnosis of CD during hospitalization, intra-
abdominal surgery, and a gastroenterologist visit within the prior year. They developed
prediction model with good performance (c-statistic 0.769). The model still performed relatively
well after internal validated using bootstrapping (c-statistic 0.726). All the risk factors identified
have biologic/clinical plausibility and fit with prior literature and clinical experience. As the
authors correctly state “the models have the potential to improve identification of persons with
CD at risk of early re-hospitalization, thereby permitting targeted outpatient post-discharge
intervention”
At this point in time, I do not recommend any further changes to the manuscript. However, there
are several limitations which the authors have correctly identified and appropriately addressed in
their limitation section, but that I wanted to emphasize regardless. Addressing these limitations
is, in my opinion, beyond the scope of this manuscript but should be considered for future work.
1) The article relies heavily on data from a single tertiary care hospital in Ottawa, Canada.
This limited geographical scope may not fully represent the diverse patient populations seen
in other healthcare settings. Future work should focus on validating these findings at another
geographically distinct academic center. While the authors took steps to perform internal
validation using bootstrapping, there approach lacks external validation on an independent
dataset. External validation is crucial to assess how well the model performs in different
healthcare settings and patient populations.
2) Using the Youden Index to maximize sensitivity and specificity in predictive models is a
mathematically sound approach; however, it has certain limitations when considered from a
clinical perspective. The primary concern is that it places equal weight on minimizing both
false negatives and false positives. Maximizing sensitivity or "casting a wider net," is crucial in
clinical applications because it minimizes the risk of failing to identify individuals who could
benefit from intervention or treatment (i.e., reducing false negatives). In healthcare, missing
a true positive case can have more serious consequences than having a few false positives.
Therefore, a clinically relevant approach may involve setting a threshold that prioritizes high
sensitivity, even at the cost of decreased specificity. This may be more relevant when developing
a scoring system as opposed to just identifying risk factors but still may have important
implications for your final model selection.
Title: CLINICAL AND HEALTH CARE UTILIZATION VARIABLES CAN PREDICT 90-
DAY HOSPITAL RE-ADMISSION IN ADULTS WITH CROHN’S DISEASE FOR POINT
OF CARE RISK EVALUATION
Version: 0 Date: 09 Dec 2023
Reviewer's report:
Dziegielewski et al. performed a retrospective study of people with CD admitted to Ottawa
Hospital between 2009–2016 will the goal of developing a multivariable risk prediction model
for 90-day hospital re-admission.
Overall, this article is very well-structured, identified a clear clinical gap with focused aims
to address the gap, detailed and appropriate methodology, and informative (and potentially
actionable) results. The study identified several factors associated with re-admission risk
including prior CD-related hospitalizations, new diagnosis of CD during hospitalization, intra-
abdominal surgery, and a gastroenterologist visit within the prior year. They developed
prediction model with good performance (c-statistic 0.769). The model still performed relatively
well after internal validated using bootstrapping (c-statistic 0.726). All the risk factors identified
have biologic/clinical plausibility and fit with prior literature and clinical experience. As the
authors correctly state “the models have the potential to improve identification of persons with
CD at risk of early re-hospitalization, thereby permitting targeted outpatient post-discharge
intervention”
At this point in time, I do not recommend any further changes to the manuscript. However, there
are several limitations which the authors have correctly identified and appropriately addressed in
their limitation section, but that I wanted to emphasize regardless. Addressing these limitations
is, in my opinion, beyond the scope of this manuscript but should be considered for future work.
1) The article relies heavily on data from a single tertiary care hospital in Ottawa, Canada.
This limited geographical scope may not fully represent the diverse patient populations seen
in other healthcare settings. Future work should focus on validating these findings at another
geographically distinct academic center. While the authors took steps to perform internal
validation using bootstrapping, there approach lacks external validation on an independent
dataset. External validation is crucial to assess how well the model performs in different
healthcare settings and patient populations.
2) Using the Youden Index to maximize sensitivity and specificity in predictive models is a
mathematically sound approach; however, it has certain limitations when considered from a
clinical perspective. The primary concern is that it places equal weight on minimizing both
false negatives and false positives. Maximizing sensitivity or "casting a wider net," is crucial in
clinical applications because it minimizes the risk of failing to identify individuals who could
benefit from intervention or treatment (i.e., reducing false negatives). In healthcare, missing
a true positive case can have more serious consequences than having a few false positives.
Therefore, a clinically relevant approach may involve setting a threshold that prioritizes high
sensitivity, even at the cost of decreased specificity. This may be more relevant when developing
a scoring system as opposed to just identifying risk factors but still may have important
implications for your final model selection.
Please review this paper.
user
Title: CLINICAL AND HEALTH CARE UTILIZATION VARIABLES CAN PREDICT 90-
DAY HOSPITAL RE-ADMISSION IN ADULTS WITH CROHN’S DISEASE FOR POINT
OF CARE RISK EVALUATION
Version: 0 Date: 09 Dec 2023
Reviewer's report:
Dziegielewski et al. performed a retrospective study of people with CD admitted to Ottawa
Hospital between 2009–2016 will the goal of developing a multivariable risk prediction model
for 90-day hospital re-admission.
Overall, this article is very well-structured, identified a clear clinical gap with focused aims
to address the gap, detailed and appropriate methodology, and informative (and potentially
actionable) results. The study identified several factors associated with re-admission risk
including prior CD-related hospitalizations, new diagnosis of CD during hospitalization, intra-
abdominal surgery, and a gastroenterologist visit within the prior year. They developed
prediction model with good performance (c-statistic 0.769). The model still performed relatively
well after internal validated using bootstrapping (c-statistic 0.726). All the risk factors identified
have biologic/clinical plausibility and fit with prior literature and clinical experience. As the
authors correctly state “the models have the potential to improve identification of persons with
CD at risk of early re-hospitalization, thereby permitting targeted outpatient post-discharge
intervention”
At this point in time, I do not recommend any further changes to the manuscript. However, there
are several limitations which the authors have correctly identified and appropriately addressed in
their limitation section, but that I wanted to emphasize regardless. Addressing these limitations
is, in my opinion, beyond the scope of this manuscript but should be considered for future work.
1) The article relies heavily on data from a single tertiary care hospital in Ottawa, Canada.
This limited geographical scope may not fully represent the diverse patient populations seen
in other healthcare settings. Future work should focus on validating these findings at another
geographically distinct academic center. While the authors took steps to perform internal
validation using bootstrapping, there approach lacks external validation on an independent
dataset. External validation is crucial to assess how well the model performs in different
healthcare settings and patient populations.
2) Using the Youden Index to maximize sensitivity and specificity in predictive models is a
mathematically sound approach; however, it has certain limitations when considered from a
clinical perspective. The primary concern is that it places equal weight on minimizing both
false negatives and false positives. Maximizing sensitivity or "casting a wider net," is crucial in
clinical applications because it minimizes the risk of failing to identify individuals who could
benefit from intervention or treatment (i.e., reducing false negatives). In healthcare, missing
a true positive case can have more serious consequences than having a few false positives.
Therefore, a clinically relevant approach may involve setting a threshold that prioritizes high
sensitivity, even at the cost of decreased specificity. This may be more relevant when developing
a scoring system as opposed to just identifying risk factors but still may have important
implications for your final model selection.
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