Quantifying prognosis with risk predictions:

Linking preoperative and early intensive care unit data for prolonged intubation prediction

Objectives

Prolonged intubation (PI) is a frequently encountered severe complication among patients following cardiac surgery (CS). Solely concentrating on preoperative data, devoid of sufficient consideration for the ongoing impact of surgical, anesthetic, and cardiopulmonary bypass procedures on subsequent respiratory system function, could potentially compromise the predictive accuracy of disease prognosis. In response to this challenge, we formulated and externally validated an intelligible prediction model tailored for CS patients, leveraging both preoperative information and early intensive care unit (ICU) data to facilitate early prophylaxis for PI.

Methods

We conducted a retrospective cohort study, analyzing adult patients who underwent CS and utilizing data from two publicly available ICU databases, namely, the Medical Information Mart for Intensive Care and the eICU Collaborative Research Database. PI was defined as necessitating intubation for over 24 h. The predictive model was constructed using multivariable logistic regression. External validation of the model's predictive performance was conducted, and the findings were elucidated through visualization techniques.

Results

The incidence rates of PI in the training, testing, and external validation cohorts were 11.8%, 12.1%, and 17.5%, respectively. We identified 11 predictive factors associated with PI following CS: plateau pressure [odds ratio (OR), 1.133; 95% confidence interval (CI), 1.111-1.157], lactate level (OR, 1.131; 95% CI, 1.067-1.2), Charlson Comorbidity Index (OR, 1.166; 95% CI, 1.115-1.219), Sequential Organ Failure Assessment score (OR, 1.096; 95% CI, 1.061-1.132), central venous pressure (OR, 1.052; 95% CI, 1.033-1.073), anion gap (OR, 1.075; 95% CI, 1.043-1.107), positive end-expiratory pressure (OR, 1.087; 95% CI, 1.047-1.129), vasopressor usage (OR, 1.521; 95% CI, 1.23-1.879), Visual Analog Scale score (OR, 0.928; 95% CI, 0.893-0.964), pH value (OR, 0.757; 95% CI, 0.629-0.913), and blood urea nitrogen level (OR, 1.011; 95% CI, 1.003-1.02). The model exhibited an area under the receiver operating characteristic curve (AUROC) of 0.853 (95% CI, 0.840-0.865) in the training cohort, 0.867 (95% CI, 0.853-0.882) in the testing cohort, and 0.704 (95% CI, 0.679-0.727) in the external validation cohort.

Conclusions

Through multicenter internal and external validation, our model, which integrates early ICU data and preoperative information, exhibited outstanding discriminative capability. This integration allows for the accurate assessment of PI risk in the initial phases following CS, facilitating timely interventions to mitigate adverse outcomes.

A Clinical Prediction Model for Postoperative Pneumonia After Lung Cancer Surgery

INTRODUCTION

Postoperative pneumonia (POP) is a common complication following lung cancer surgery and is associated with increased hospitalization costs and mortalities. We aimed to identify risk factors associated with POP and to develop a reliable predictive model.

METHODS

Patients who underwent lung cancer surgery between January 2015 and December 2021 in our hospital were enrolled. Least absolute shrinkage and selection operator regression analysis was used to select predictors of POP. Multivariable logistic regression was performed to construct the nomogram. Bootstrap resampling was conducted for internal validation. The performance of the model was evaluated by discrimination and calibration.

RESULTS

A total of 5269 consecutive patients were enrolled. POP occurred in 1.7% of patients (92/5269). Five independent predictors were identified: age, predicted forced expiratory volume in 1 s, predicted diffusing capacity of the lungs for carbon monoxide, tuberculosis history, and surgery duration. The multivariable regression model showed good discrimination (C-index: 0.821, 95% confidence interval, 0.783-0.859), which was well validated by internal validation. The calibration curve illustrated good agreement between the predicted probability and observed probability of POP.

CONCLUSIONS

Based on the easily available risk factors, our nomogram could predict the risk of POP with good discrimination and calibration. The model has good clinical practicability, enabling precise and targeted interventions to reduce the incidence of POP in high-risk patients.

Would surgical Apgar score be useful to predict postoperative complications after proximal femoral fracture surgery? - A retrospective cohort study

BACKGROUND

The surgical Apgar score (SAS) is a perioperative risk evaluation score, which considers intraoperative minimum heart rate, minimum mean arterial pressure and estimated blood loss. Although validated in multiple surgical fields, SAS remains quite controversial in the orthopedic one. The main purpose of this study was to investigate if SAS relates with the occurrence of complications during the first 30-days after proximal femoral fracture surgery.

METHODS

Retrospective study including all consecutive patients submitted to proximal femoral fracture surgery between January and July 2019. Patients with no information about SAS were excluded. Patients were divided in two groups, based on the occurrence of complications during the first 30 post-operative days and their SAS calculated. Receiver operating characteristic (ROC) curves were used to assess SAS power as a predictive model of complications.

RESULTS

Forty-two percent (n = 76) of the 181 patients included in the study developed complications during the first 30 postoperative days. Eight patients (4,4%) died during that period. The patient's mean age was 79 years and 30,9% (n = 56) were men. Heart failure, pacemaker use, chronic kidney disease, chronic obstructive pulmonary disease and dementia were significantly associated with post-operative morbidity. There was no significant correlation between SAS and the occurrence of complications during the first 30 postoperative days. The AUC of SAS as a predictive model for postoperative complications after proximal femoral fracture surgery was 0,522, being insufficient to be considered an accepted model of prediction.

CONCLUSION

Based on this study, we conclude that SAS is not predictive of the development of complications in the first 30 post-operative days in patients submitted to proximal femoral fracture surgery. However, other clinical factors have been identified as associated with postoperative morbidity. In the future, prospective-based studies with higher samples may better clarify the role of SAS in this context.

Methodological standards for the development and evaluation of clinical prediction rules: a review of the literature

Clinical prediction rules (CPRs) that predict the absolute risk of a clinical condition or future outcome for individual patients are abundant in the medical literature; however, systematic reviews have demonstrated shortcomings in the methodological quality and reporting of prediction studies. To maximise the potential and clinical usefulness of CPRs, they must be rigorously developed and validated, and their impact on clinical practice and patient outcomes must be evaluated. This review aims to present a comprehensive overview of the stages involved in the development, validation and evaluation of CPRs, and to describe in detail the methodological standards required at each stage, illustrated with examples where appropriate. Important features of the study design, statistical analysis, modelling strategy, data collection, performance assessment, CPR presentation and reporting are discussed, in addition to other, often overlooked aspects such as the acceptability, cost-effectiveness and longer-term implementation of CPRs, and their comparison with clinical judgement. Although the development and evaluation of a robust, clinically useful CPR is anything but straightforward, adherence to the plethora of methodological standards, recommendations and frameworks at each stage will assist in the development of a rigorous CPR that has the potential to contribute usefully to clinical practice and decision-making and have a positive impact on patient care.

Tools, Clinical Prediction Rules, and Algorithms for the Insertion of Peripheral Intravenous Catheters in Adult Hospitalized Patients: A Systematic Scoping Review of Literature

BACKGROUND

First-time peripheral intravenous catheter (PIVC) insertion success is dependent on patient, clinician, and product factors. Failed PIVC insertion are an under-recognized clinical phenomenon.

OBJECTIVE

To provide a scoping review of decision aids for PIVC insertion including tools, clinical prediction rules, and algorithms (TRAs) and their findings on factors associated with insertion success.

METHODS

In June 2016, a systematic literature search was performed using the medical subject heading of peripheral catheterization and tool* or rule* or algorithm*. Data extraction included clinician, patient, and/or product variables associated with PIVC insertion success. Information about TRA reliability, validity, responsiveness, and utility was also extracted.

RESULTS

We screened 36 studies, and included 13 for review. Seven papers reported insertion success ranging from 61%-90% (4030 insertion attempts), 6 on validity, and 5 on reliability, with none reporting on responsiveness and utility. Failed insertions were associated with obesity (odds ratio [OR], 0.71-1.7; 2 studies) and smaller gauge PIVCs (OR, 6.4; 95% Confidence Interval [CI}, 3.4-11.9). Successful inser tions were associated with visible veins (OR, 0.87-3.63; 3 studies) or palpable veins (OR, 0.79-5.05; 3 studies) and inserters with greater procedural volume (OR, 4.4; 95% CI, 1.6-12.1) or who predicted that insertion would be successful (OR, 1.06; 95% CI, 1.04-1.07). Definitions of insertion difficulty are heterogeneous such as time to insert to a number of failed attempts.

CONCLUSIONS

Few well-validated reliable TRAs exist for PIVC insertion. Patients would benefit from a validated, clinically pragmatic TRA that matches insertion difficulty with clinician competency.

Risk stratification after ST-segment elevation myocardial infarction

INTRODUCTION

Risk stratification according to the timing of assessment, treatment modality and outcome of interest is highly advisable in patients with ST-elevation myocardial infarction (STEMI) to identify optimal treatment strategies, proper length of hospital stay and correct timing of follow-up. Areas covered: This review is an overview summarizing the characteristics and performance of available risk-scoring systems for STEMI. In particular, we sought to highlight the characteristics of STEMI cohorts used for derivation and validation of the available algorithms and appraise their discrimination ability, calibration and global accuracy. Expert commentary: Applying the appropriate score, customized on patients' profile and clinical characteristics at presentation or during the hospitalization, might prove useful to improve the overall quality of care provided to STEMI patients.

Predicting postoperative pulmonary complications in the general population

PURPOSE OF REVIEW

Postoperative pulmonary complications (PPCs) are common and lead to longer hospital stays and higher mortality. A wide range of patient, anaesthetic and surgical factors have been associated with risk for PPCs. This review discusses our present understanding of PPC risk factors that can be used to plan preoperative risk reduction strategies. The methodological and statistical basis for building risk scores is also described.

RECENT FINDINGS

Studies in specific surgical populations or large patient samples have identified a range of predictors of PPC risk. Factors such as age, types of comorbidity and surgical characteristics have been found to be relevant in most of these studies. Recently, researchers have begun to develop risk scoring systems for a PPC composite outcome or for specific PPCs, especially pneumonia and respiratory failure. Preoperative arterial oxyhaemoglobin saturation is an objective measure that is easy to record and discriminates level of risk for impaired cardiorespiratory function. Preoperative anaemia and recent respiratory infection are factors that have lately been found to confer risk for PPCs.

SUMMARY

PPC risk prediction scales based on large population studies are being developed. New studies to confirm the validity of these scales in different geographic areas will be needed before we can be sure of their generalizability.

Preoperative prediction of acute kidney injury--from clinical scores to biomarkers

Early acute kidney injury (AKI) diagnosis in critically ill children has been an important recent research focus because of the known association of AKI with poor outcomes and the requirement of early intervention to mitigate negative effects of AKI. In children having surgery, the preoperative period offers a unique opportunity to predict postoperative acute kidney injury (AKI), well before AKI occurs. Pediatric AKI epidemiologic studies have begun to identify which preoperative factors may predict development of postoperative cardiac surgery. Using these clinical risk factors, it may be possible to derive preoperative clinical risk scores and improve upon our ability to risk-stratify children into AKI treatment trials, pre-emptively provide conservative renal injury prevention strategies, and ultimately improve patient outcomes. Developing risk scores requires rigorous methodology and validation before widespread use. There is little information currently on the use of preoperative biological or physiological biomarkers to predict postoperative AKI, representing an important area of future research. This review will provide an overview of methodology of preoperative risk score development, discuss pediatric-specific issues around deriving such risk scores, including the combination of preoperative clinical and biologic biomarkers for AKI prediction, and suggest future research avenues.