Predicting medical complications in spine surgery: evaluation of a novel online risk calculator

Kasparek, Michael S.; Boettner, Friedrich; Rienmueller, Anna; Weber, Michael; Funovics, Philipp T.; Krepler, P; Windhager, Reinhard; Grohs, Josef G.

doi:10.1007/s00586-018-5707-9

Cited by 27 publications

(16 citation statements)

References 20 publications

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“…This calculator was further assessed by Kasparek et al on 273 patients and found similar AUC values of 0.85 for medical complications and 0.71 for major medical complications. The authors concluded that the calculator was accurate at determining complications following various spine procedures [14,15]. The benefits of this calculator are that it includes several surgery-specific variables and patient comorbidities.…”

Section: Spinesagementioning

confidence: 99%

“…From a quality improvement perspective, risk estimation may help reduce adverse events and The Centers for Medicare and Medicaid Services (CMS) may soon incentivize surgeons to incorporate risk assessment tools into the informed consent process for elective surgery [4]. Growing interest and emphasis on the importance of risk prediction in the orthopedic community have led to the development of several automated risk calculators [5][6][7][8][9][10][11][12][13][14][15][16][17]. Automated risk calculators allow the user to input specific patient characteristics into a calculator for a specific procedure to produce an automated risk prediction of adverse outcomes.…”

Section: Introductionmentioning

confidence: 99%

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Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery

Merrill

Ibrahim

Machi

et al. 2020

Curr Rev Musculoskelet Med

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Purpose of Review To discuss the automated risk calculators that have been developed and evaluated in orthopedic surgery. Recent Findings Identifying predictors of adverse outcomes following orthopedic surgery is vital in the decision-making process for surgeons and patients. Recently, automated risk calculators have been developed to quantify patient-specific preoperative risk associated with certain orthopedic procedures. Automated risk calculators may provide the orthopedic surgeon with a valuable tool for clinical decision-making, informed consent, and the shared decision-making process with the patient. Understanding how an automated risk calculator was developed is arguably as important as the performance of the calculator. Additionally, conveying and interpreting the results of these risk calculators with the patient and its influence on surgical decision-making are paramount. Summary The most abundant research on automated risk calculators has been conducted in the spine, total hip and knee arthroplasty, and trauma literature. Currently, many risk calculators show promise, but much research is still needed to improve them. We recommend they be used only as adjuncts to clinical decision-making. Understanding how a calculator was developed, and accurate communication of results to the patient, is paramount.

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Section: Spinesagementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery

Merrill

Ibrahim

Machi

et al. 2020

Curr Rev Musculoskelet Med

View full text Add to dashboard Cite

show abstract

“…Kasparek and colleagues demonstrated the clinical applicability and effectiveness of an online spinal surgery complication risk calculator within 30 days of surgery for a prospective analysis of 273 patients. 35 They demonstrated a prediction of 14.7% versus the reported 16.1% for medical complications and 3.8% for major medical complications versus the reported 3.3% in the trial. AUC values were 0.71 and 0.85 for medical and major medical complications, respectively.…”

Section: Synthesis and Suggestionsmentioning

confidence: 87%

“…AUC values were 0.71 and 0.85 for medical and major medical complications, respectively. 35 Interestingly, those deformity predictive models that included radiographic features such as pelvic tilt or instrumented levels reported higher AUC values than those that strictly referenced patient demographic and medical comorbidities. Belykh and colleagues also relied heavily on radiographic features to yield a highly accurate predictive model with 90% accuracy using disk-height index, segmental range of motion, and Pfirrmann grade, for example.…”

Section: Synthesis and Suggestionsmentioning

confidence: 98%

Evaluation of Predictive Models for Complications following Spinal Surgery

Dietz

Sharma

Alhourani

et al. 2020

J Neurol Surg A Cent Eur Neurosurg

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Background Complications rates vary across spinal surgery procedures and are difficult to predict due to heterogeneity in patient characteristics, surgical methods, and hospital volume. Incorporation of predictive models for complications may guide surgeon decision making and improve outcomes. Methods We evaluate current independently validated predictive models for complications in spinal surgery with respect to study design and model generation, accuracy, reliability, and utility. We conducted our search using Preferred Reporting Items for Systematic Review and Meta-analysis guidelines and the Participants, Intervention, Comparison, Outcomes, Study Design model through the PubMed and Ovid Medline databases. Results A total of 18 articles met inclusion criteria including 30 validated predictive models of complications after adult spinal surgery. National registry databases were used in 12 studies. Validation cohorts were used in seven studies for verification; three studies used other methods including random sample bootstrapping techniques or cross-validation. Reported area under the curve (AUC) values ranged from 0.37 to 1.0. Studies described treatment for deformity, degenerative conditions, inclusive spinal surgery (neoplasm, trauma, infection, deformity, degenerative), and miscellaneous (disk herniation, spinal epidural abscess). The most commonly cited risk factors for complications included in predictive models included age, body mass index, diabetes, sex, and smoking. Those models in the deformity subset that included radiographic and anatomical grading features reported higher AUC values than those that included patient demographics or medical comorbidities alone. Conclusions We identified a cohort of 30 validated predictive models of complications following spinal surgery for degenerative conditions, deformity, infection, and trauma. Accurate evidence-based predictive models may enhance shared decision making, improve rehabilitation, reduce adverse events, and inform best practices.

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“…Prone positioning during the surgery is associated with physiological changes and the surgery itself is associated with significant intraoperative blood loss and postoperative complications. [17][18][19] Surprisingly, little is known about the impact of IFM in this specific context. Therefore, we designed a before-after comparison study to investigate the impact of IFM implementation on the postoperative outcome of patients undergoing major spine surgery.…”

Section: Introductionmentioning

confidence: 99%

Outcome impact of individualized fluid management during spine surgery A before-after prospective comparison study

Che

Zhang

et al. 2020

Preprint

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Background Individualized fluid management (IFM) has been shown to be useful to improve the postoperative outcome of patients undergoing major abdominal surgery. A limited number of clinical studies have been done in orthopaedic patients and have yielded conflicting results. We designed the present study to investigate the clinical impact of IFM in patients undergoing major spine surgery. Methods This is a before-after study done in 300 patients undergoing posterior spine arthrodesis. Postoperative outcomes were compared between control group implementing standard fluid management (n = 150) and IFM group (n = 150) guided by fluid protocol based on continuous stroke volume monitoring and optimization. The primary outcome measure was the proportion of patients who developed one or more complications within 30 days following surgery. Results During surgery, patients received on average the same volume of crystalloids (7.4 vs 7.2 ml/kg/h) and colloids (1.6 vs 1.6 ml/kg/h) before and after the implementation of IFM. During 30 days following surgery, the proportion of patients who developed one or more complications was lower in the IFM group (32 vs 48%, p<0.01). This difference was mainly explained by a significant decrease in post-operative nausea and vomiting (from 38 to 19%, p<0.01), urinary tract infections (from 9 to 1%, p<0.01) and surgical site infections (from 5 to 1%, p<0.05). Median hospital length of stay was not affected by the implementation of IFM. Conclusions In patients undergoing major spine surgery, the implementation of IFM was associated with a significant decrease in postoperative morbidity.

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Predicting medical complications in spine surgery: evaluation of a novel online risk calculator

Cited by 27 publications

References 20 publications

Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery

Analysis and Review of Automated Risk Calculators Used to Predict Postoperative Complications After Orthopedic Surgery

Evaluation of Predictive Models for Complications following Spinal Surgery

Outcome impact of individualized fluid management during spine surgery A before-after prospective comparison study

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