A clinical nomogram predicting unplanned intensive care unit admission after hip fracture surgery

The hip fracture assessment tool: A scoring system to assess high risk geriatric hip fracture patients for post-operative critical care monitoring

INTRODUCTION

Intensive care unit risk stratification models have been utilized in elective joint arthroplasty; however, hip fracture patients are fundamentally different in their clinical course. Having a critical care risk calculator utilizing pre-operative risk factors can improve resourcing for hip fracture patients in the peri‑operative period.

METHODS

A cohort of geriatric hip fracture patients at a single institution were reviewed over a three-year period. Non-operative patients, peri‑implant fractures, additional procedures performed under the same anesthesia period, and patients admitted to the intensive care unit (ICU) prior to surgery were excluded. Pre-operative laboratory values, Revised Cardiac Risk Index (RCRI), and American Society of Anesthesiologists (ASA) scores were calculated. Pre-operative ambulatory status was determined. The primary outcome measure was ICU admission in the post-operative period. Outcomes were assessed with Fisher's exact test, Kruskal-Wallis test, logistic regression, and ROC curve.

RESULTS

315 patient charts were analyzed with 262 patients meeting inclusion criteria. Age ≥ 80 years, ASA ≥ 4, pre-operative hemoglobin < 10 g/dL, and a history of CVA/TIA were found to be significant factors and utilized within a "training" data set to create a 4-point scoring system after reverse stepwise elimination. The 4-point scoring system was then assessed within a separate "validation" data set to yield an ROC area under the curve (AUC) of 0.747. Score ≥ 3 was associated with 96.8 % specificity and 14.2 % sensitivity for predicting post-op ICU admission. Score ≥ 3 was associated with a 50 % positive predictive value and 83 % negative predictive value.

CONCLUSION

A hip fracture risk stratification scoring system utilizing pre-operative patient specific values to stratify geriatric hip patients to the ICU post-operatively can improve the pre-operative decision-making of surgical and critical care teams. This has important implications for triaging vital hospital resources.

LEVEL OF EVIDENCE

III (retrospective study).

Retrospective Analysis of Risk Factors in Geriatric Hip Fracture Patients Predictive of Surgical Intensive Care Unit Admission

INTRODUCTION

Although numerous risk factors and prediction models affecting morbidity and mortality in geriatric hip fracture patients have been previously identified, there are scant published data on predictors for perioperative Surgical Intensive Care Unit (SICU) admission in this patient population. Determining if a patient will need an SICU admission would not only allow for the appropriate allocation of resources and personnel but also permit targeted clinical management of these patients with the goal of improving morbidity and mortality outcomes. The purpose of this study was to identify specific risk factors predictive of SICU admission in a population of geriatric hip fracture patients. Unlike previous studies which have investigated predominantly demographic, comorbidity, and laboratory data, the present study also considered a frailty index and length of time from injury to presentation in the Emergency Department (ED).

METHODS

A total of 501 geriatric hip fracture patients admitted to a Level 1 trauma center were included in this retrospective, single-center, quantitative study from January 1, 2019, to December 31, 2022. Using a logistical regression analysis, more than 25 different variables were included in the regression model to identify values predictive of SICU admission. Predictive models of planned versus unplanned SICU admissions were also estimated. The discriminative ability of variables in the final models to predict SICU admission was assessed with receiver operating characteristic curves' area under the curve estimates.

RESULTS

 Frailty, serum lactate > 2, and presentation to the ED > 12 hours after injury were significant predictors of SICU admission overall (P = 0.03, 0.038, and 0.05 respectively). Additionally, the predictive model for planned SICU admission had no common significant predictors with unplanned SICU admission. Planned SICU admission significant predictors included an Injury Severity Score (ISS) of 15 and greater, a higher total serum protein, serum sodium <135, systolic blood pressure (BP) under 100, increased heart rate on admission to ED, thrombocytopenia (<120), and higher Anesthesia Society Association physical status classification (ASA) score (P = 0.007, 0.04, 0.05, 0.002, 0.041, 0.05, and 0.005 respectively). Each SICU prediction model (overall, planned, and unplanned) demonstrated sufficient discriminative ability with the area under the curve (AUC) values of 0.869, 0.601, and 0.866 respectively. Finally, mean hospital Length of Stay (LOS) and mortality were increased in SICU admissions when compared to non-SICU admissions.

CONCLUSION

Of the three risk factors predictive of SICU admission identified in this study, two have not been extensively studied previously in this patient population. Frailty has been associated with increased mortality and postoperative complications in hip fracture patients, but this is the first study to date to use a novel frailty index specifically designed and validated for use in hip fracture patients. The other risk factor, time from injury to presentation to the ED serves as an indicator for time a hip fracture patient spent without receiving medical attention. This risk factor has not been investigated heavily in the past as a predictor of SICU admissions in this patient population.

Development and validation of a nomogram for predicting in-hospital mortality of patients with cervical spine fractures without spinal cord injury

BACKGROUND

The incidence of cervical spine fractures is increasing every day, causing a huge burden on society. This study aimed to develop and verify a nomogram to predict the in-hospital mortality of patients with cervical spine fractures without spinal cord injury. This could help clinicians understand the clinical outcome of such patients at an early stage and make appropriate decisions to improve their prognosis.

METHODS

This study included 394 patients with cervical spine fractures from the Medical Information Mart for Intensive Care III database, and 40 clinical indicators of each patient on the first day of admission to the intensive care unit were collected. The independent risk factors were screened using the Least Absolute Shrinkage and Selection Operator regression analysis method, a multi-factor logistic regression model was established, nomograms were developed, and internal validation was performed. A receiver operating characteristic (ROC) curve was drawn, and the area under the ROC curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were calculated to evaluate the discrimination of the model. Moreover, the consistency between the actual probability and predicted probability was reflected using the calibration curve and Hosmer-Lemeshow (HL) test. A decision curve analysis (DCA) was performed, and the nomogram was compared with the scoring system commonly used in clinical practice to evaluate the clinical net benefit.

RESULTS

The nomogram indicators included the systolic blood pressure, oxygen saturation, respiratory rate, bicarbonate, and simplified acute physiology score (SAPS) II. The results showed that our model had satisfactory predictive ability, with an AUC of 0.907 (95% confidence interval [CI] = 0.853-0.961) and 0.856 (95% CI = 0.746-0.967) in the training set and validation set, respectively. Compared with the SAPS-II system, the NRI values of the training and validation sets of our model were 0.543 (95% CI = 0.147-0.940) and 0.784 (95% CI = 0.282-1.286), respectively. The IDI values of the training and validation sets were 0.064 (95% CI = 0.004-0.123; P = 0.037) and 0.103 (95% CI = 0.002-0.203; P = 0.046), respectively. The calibration plot and HL test results confirmed that our model prediction results showed good agreement with the actual results, where the HL test values of the training and validation sets were P = 0.8 and P = 0.95, respectively. The DCA curve revealed that our model had better clinical net benefit than the SAPS-II system.

CONCLUSION

We explored the in-hospital mortality of patients with cervical spine fractures without spinal cord injury and constructed a nomogram to predict their prognosis. This could help doctors assess the patient's status and implement interventions to improve prognosis accordingly.

The Impact of Screening for Perioperative ICU Admission in Geriatric Hip Fracture Patients: A Retrospective Analysis

BACKGROUND

Hip fracture patients are a subset of trauma patients with high peri-operative mortality. To mitigate the mortality risk, the use of predictive scoring systems (e.g., RSI or Nomograms) for risk stratification and monitoring of high-risk patients in the intensive care unit (ICU) has been proposed. Screening patients for ICU admission with relatively low-cost tools may achieve high-quality, low-cost care. The aim of this study was to assess the effectiveness and feasibility of screening postoperative hip fracture patients for ICU admission.

METHODS

This is a retrospective single-site study comparing two groups of patients, before and after implementation of a hip fracture postoperative screening intervention in a level 1 trauma center in the United States. All hip fracture patients > 55 years of age admitted to the hospital between January 2021 and May 2023 were included. Trauma team members assessed and screened patients postoperatively in the post-anesthesia care unit (PACU), ordering standardized tests, including laboratory tests, a chest x-ray, and electrocardiogram (EKG). Assessment of the effect of the intervention included a comparison of a number of major adverse events (MAEs), mortality, planned and unplanned ICU admissions, ICU length of stay (LOS), and hospital LOS between pre- and post-intervention groups. Propensity score (PS) estimates were used to compare outcomes between the matched participants in the sample. A predictive model for ICU admission for the overall sample was estimated, and discriminative ability was assessed with an area under the curve (AUC) receiver operator characteristics (ROC) analysis. Lastly, feasibility was assessed by compliance with screening intervention and charges per patient related to the intervention.

RESULTS

The sample consisted of 290 patients in the pre-intervention and 180 patients in the post-intervention groups, respectively, with a mean age of 81.4 ± (9.9) years. There was a significant increase (p<0.01) in planned ICU admissions (OR=2.387, 95% CI (1.430, 3.983)) after screening protocol implementation. There was no significant difference between the pre-intervention group and post-intervention group in the number of MAEs (p=0.392), mortality (p=0.591), ICU LOS (p=0.617), and hospital LOS (p=0.151). When the PS-matched sample (n=424) was analyzed, there was a significant decrease (p=0.45) in unplanned ICU admissions (OR=6.40, 95% CI (0.81, 50.95)) after protocol implementation. Anticoagulants, emergency department (ED) respiratory rate (RR), injury severity score (ISS), number of comorbidities, substance use disorder (SAD), peripheral artery disease (PAD), and chronic obstructive pulmonary disease (COPD) were significant predictors of ICU admission (p=0.002, 0.022, 0.030, 0.034, 0.039, 0.039, and 0.042), respectively, and, demonstrated the discriminative ability between high and low risk for ICU admission (AUC=0.597, 0.587, 0.581, 0.578, 0.513, and 0.587, respectively). The screening intervention was achievable with 99% compliance (Kappa estimate 0.94) among trauma team members with an average charge of $282 per patient.

CONCLUSION

The addition of a postoperative screening intervention for hip fracture patients > 55 years of age is achievable and decreases unplanned ICU admissions in matched samples. Presenting clinical indicators and comorbidities are associated with ICU admission and provide sufficient discriminatory ability as criteria for ICU admission.

Development and Internal Validation of a Nomogram to Predict Mortality During the ICU Stay of Thoracic Fracture Patients Without Neurological Compromise: An Analysis of the MIMIC-III Clinical Database

Background: This study aimed to develop and validate a nomogram for predicting mortality in patients with thoracic fractures without neurological compromise and hospitalized in the intensive care unit. Methods: A total of 298 patients from the Medical Information Mart for Intensive Care III (MIMIC-III) database were included in the study, and 35 clinical indicators were collected within 24 h of patient admission. Risk factors were identified using the least absolute shrinkage and selection operator (LASSO) regression. A multivariate logistic regression model was established, and a nomogram was constructed. Internal validation was performed by the 1,000 bootstrap samples; a receiver operating curve (ROC) was plotted, and the area under the curve (AUC), sensitivity, and specificity were calculated. In addition, the calibration of our model was evaluated by the calibration curve and Hosmer-Lemeshow goodness-of-fit test (HL test). A decision curve analysis (DCA) was performed, and the nomogram was compared with scoring systems commonly used during clinical practice to assess the net clinical benefit. Results: Indicators included in the nomogram were age, OASIS score, SAPS II score, respiratory rate, partial thromboplastin time (PTT), cardiac arrhythmias, and fluid-electrolyte disorders. The results showed that our model yielded satisfied diagnostic performance with an AUC value of 0.902 and 0.883 using the training set and on internal validation. The calibration curve and the Hosmer-Lemeshow goodness-of-fit (HL). The HL tests exhibited satisfactory concordance between predicted and actual outcomes (P = 0.648). The DCA showed a superior net clinical benefit of our model over previously reported scoring systems. Conclusion: In summary, we explored the incidence of mortality during the ICU stay of thoracic fracture patients without neurological compromise and developed a prediction model that facilitates clinical decision making. However, external validation will be needed in the future.