Modified abbreviated burn severity index as a predictor of in-hospital mortality in patients with inhalation injury: development and validation using independent cohorts

Early extubations in children intubated prior to arrival in Paediatric Burn ICU: A single center retrospective study over 1520 admissions

BACKGROUND

In adult burns intensive care units, more than 30% of patients arriving intubated, are extubated within 2 days (potentially unnecessary intubation, PUNI). Such data are lacking in paediatric populations. Exploring this paediatric PUNI rate was the primary aim of the study.

METHODS

Data from all the admissions to our paediatric burn intensive care unit were retrospectively analyzed over an 8-years period. Extubations within the first two days among patients arriving intubated were assessed as the primary outcome (PUNI rate). Using a univariate logistic regression and a multivariate model, we analyzed factors associated with intubation lasting more than 2 days (potentially necessary intubation, PNI). Finally, we developed a score to predict the probability of PNI.

RESULTS

Among the 1520 admitted children (age: 0-17; Percentage of Total Body Surface Area (%TBSA): 1%-97%), 56 (4%) arrived intubated, 20 (36%) of whom were considered PUNI. These patients had smaller %TBSA burned compared to those having PNI (24% ± 17% vs. 48% ± 24%, p = 0.002). We developed a score based on factors independently associated with PNI: %TBSA burned (OR = 1.12 [1.09-1.15] for each additional per cent), flame burns (OR = 4.43 [1.64-11.6]) and facial burns (OR = 12.28 [3.41-67.4]). Seven children (<0.5%) were intubated after admission.

CONCLUSION

Intubation before admission to a burn intensive care unit was less frequent in children. The paediatric rate of PUNI, however, was close to findings reported in adults: approximately one-third of intubated children were extubated within 2 days.

Revalidating the prognostic relevance of the Abbreviated Burn Severity Index (ABSI): A twenty-year experience examining the performance of the ABSI score in consideration of progression and advantages of burn treatments from a single center in Vienna

BACKGROUND

The Abbreviated Burn Severity Index (ABSI) is a five-variable scale to help evaluate burn severity upon initial assessment. As other studies have been conducted with comparatively small patient populations, the purpose of this study is to revalidate the prognostic relevance of the ABSI in our selected population (N = 1193) 4 decades after its introduction, considering the progress in the treatment of severe burn injuries over the past decades. In addition, we evaluate whether comorbidities influence the survival probability of severely burned patients.

METHODS

This retrospective study presents data from the Center for Severely Burned Patients of the General Hospital in Vienna. We included 1193 patients for over 20 years. Regression models were used to describe the prognostic accuracy of the ABSI.

RESULTS

The ABSI can still be used as a prognostic factor for the probability of survival of severely burned patients. The odds of passing increases by a factor of 2.059 for each unit increase in the ABSI with an area under the curve value of 0.909. Over time, the likelihood of survival increased. The existence of chronic kidney disease negatively impacts the survival probability of severely burned patients.

CONCLUSION

The ABSI can still be used to provide accurate information about the chances of survival of severely burned patients; however, further exploration of the impact of chronic kidney disease on the survival probability and adding variables to the ABSI scale should be considered. The probability of survival has increased over the last 20 years.

Models predicting mortality risk of patients with burns to ≥ 50% of the total body surface

BACKGROUND

Several models predicting mortality risk of burn patients have been proposed. However, models that consider all such patients may not well predict the mortality of patients with extensive burns.

METHOD

This retrospective multicentre study recruited patients with extensive burns (≥ 50% of the total body surface area [TBSA]) treated in three hospitals of Eastern China from 1 January 2016 to 30 June 2022. The performances of six predictive models were assessed by drawing receiver operating characteristic (ROC) and calibration curves. Potential predictors were sought via "least absolute shrinkage and selection operator" regression. Multivariate logistic regression was employed to construct a predictive model for patients with burns to ≥ 50% of the TBSA. A nomogram was prepared and the performance thereof assessed by reference to the ROC, calibration, and decision curves.

RESULT

A total of 465 eligible patients with burns to ≥ 50% TBSA were included, of whom 139 (29.9%) died. The FLAMES model exhibited the largest area under the ROC curve (AUC) (0.875), followed by the models of Zhou et al. (0.853) and the ABSI model (0.802). The calibration curve of the Zhou et al. model fitted well; those of the other models significantly overestimated the mortality risk. The new nomogram includes four variables: age, the %TBSA burned, the area of full-thickness burns, and blood lactate. The AUCs (training set 0.889; internal validation set 0.934; external validation set 0.890) and calibration curves showed that the nomogram exhibited an excellent discriminative capacity and that the predictions were very accurate.

CONCLUSION

For patients with burns to ≥ 50％of the TBSA, the Zhou et al. and FLAMES models demonstrate relatively high predictive ability for mortality. The new nomogram is sensitive, specific, and accurate, and will aid rapid clinical decision-making.

Sample size requirements are not being considered in studies developing prediction models for binary outcomes: a systematic review

BACKGROUND

Having an appropriate sample size is important when developing a clinical prediction model. We aimed to review how sample size is considered in studies developing a prediction model for a binary outcome.

METHODS

We searched PubMed for studies published between 01/07/2020 and 30/07/2020 and reviewed the sample size calculations used to develop the prediction models. Using the available information, we calculated the minimum sample size that would be needed to estimate overall risk and minimise overfitting in each study and summarised the difference between the calculated and used sample size.

RESULTS

A total of 119 studies were included, of which nine studies provided sample size justification (8%). The recommended minimum sample size could be calculated for 94 studies: 73% (95% CI: 63-82%) used sample sizes lower than required to estimate overall risk and minimise overfitting including 26% studies that used sample sizes lower than required to estimate overall risk only. A similar number of studies did not meet the ≥ 10EPV criteria (75%, 95% CI: 66-84%). The median deficit of the number of events used to develop a model was 75 [IQR: 234 lower to 7 higher]) which reduced to 63 if the total available data (before any data splitting) was used [IQR:225 lower to 7 higher]. Studies that met the minimum required sample size had a median c-statistic of 0.84 (IQR:0.80 to 0.9) and studies where the minimum sample size was not met had a median c-statistic of 0.83 (IQR: 0.75 to 0.9). Studies that met the ≥ 10 EPP criteria had a median c-statistic of 0.80 (IQR: 0.73 to 0.84).

CONCLUSIONS

Prediction models are often developed with no sample size calculation, as a consequence many are too small to precisely estimate the overall risk. We encourage researchers to justify, perform and report sample size calculations when developing a prediction model.