Development and internal validation of a clinical prediction model for 90-day mortality after lung resection: the RESECT-90 score

 Open in a separate windowOBJECTIVESThe ability to accurately estimate the risk of peri-operative mortality after lung resection is important. There are concerns about the performance and validity of existing models developed for this purpose, especially when predicting mortality within 90 days of surgery. The aim of this study was therefore to develop a clinical prediction model for mortality within 90 days of undergoing lung resection.METHODSA retrospective database of patients undergoing lung resection in two UK centres between 2012 and 2018 was used to develop a multivariable logistic risk prediction model, with bootstrap sampling used for internal validation. Apparent and adjusted measures of discrimination (area under receiving operator characteristic curve) and calibration (calibration-in-the-large and calibration slope) were assessed as measures of model performance.RESULTSData were available for 6600 lung resections for model development. Predictors included in the final model were age, sex, performance status, percentage predicted diffusion capacity of the lung for carbon monoxide, anaemia, serum creatinine, pre-operative arrhythmia, right-sided resection, number of resected bronchopulmonary segments, open approach and malignant diagnosis. Good model performance was demonstrated, with adjusted area under receiving operator characteristic curve, calibration-in-the-large and calibration slope values (95% confidence intervals) of 0.741 (0.700, 0.782), 0.006 (−0.143, 0.156) and 0.870 (0.679, 1.060), respectively.CONCLUSIONSThe RESECT-90 model demonstrates good statistical performance for the prediction of 90-day mortality after lung resection. A project to facilitate large-scale external validation of the model to ensure that the model retains accuracy and is transferable to other centres in different geographical locations is currently underway.     

External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease

BACKGROUND CONTEXTPreoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG ML 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease were previously developed in a single institutional sample but remain to be externally validated.PURPOSEThe purpose of this study was to externally validate these algorithms in an independent population from another institution.STUDY DESIGN/SETTINGRetrospective study at a large, tertiary care center.PATIENT SAMPLEPatients 18 years or older who underwent surgery between 2003 and 2016.OUTCOME MEASURESNinety-day and 1-year mortality.METHODSBaseline characteristics of the validation cohort were compared to the developmental cohort for the SORG ML algorithms. Discrimination (c-statistic and receiver operating curve), calibration (calibration slope, intercept, calibration plot, and observed proportions by predicted risk groups), overall performance (Brier score), and decision curve analysis were used to assess the performance of the SORG ML algorithms in the validation cohort.RESULTSOverall, 176 patients underwent surgery for spinal metastatic disease, of which 44 (22.7%) experienced 90-day mortality and 99 (56.2%) experienced 1-year mortality. The validation cohort differed significantly from the developmental cohort on primary tumor histology, metastatic tumor burden, previous systemic therapy, overall comorbidity burden, and preoperative laboratory characteristics. Despite these differences, the SORG ML algorithms generalized well to the validation cohort on discrimination (c-statistic 0.75–0.81 for 90-day mortality and 0.77–0.78 for 1-year mortality), calibration, Brier score, and decision curve analysis.CONCLUSION and RELEVANCEInitial results from external validation of the SORG ML 90-day and 1-year algorithms for survival prediction in spinal metastatic disease suggest potential utility of these digital decision aids in clinical practice. Further studies are needed to validate or refute these algorithms in large patient samples from prospective, international, multi-institutional trials.     

Predictors of reoperation after surgery for spinal epidural abscess

BACKGROUND CONTEXTSpinal epidural abscess is a rare but severe condition with high rates of postoperative adverse events.PURPOSEThe objective of the study was to identify independent prognostic factors for reoperation using two datasets: an institutional and national database.STUDY DESIGN/SETTINGRetrospective Review.PATIENT SAMPLEDatabase 1: Review of five medical centers from 1993 to 2016. Database 2: The National Surgical Quality Improvement Program (NSQIP) was queried between 2012 and 2016.OUTCOME MEASURESThirty-day and ninety-day reoperation rate.METHODSTwo independent datasets were reviewed to identify patients with spinal epidural abscesses undergoing spinal surgery. Multivariate analyses were used to determine independent prognostic factors for reoperation while including factors identified in bivariate analyses.RESULTSOverall, 642 patients underwent surgery for a spinal epidural abscess in the institutional cohort, with a 90-day unplanned reoperation rate of 19.9%. In the NSQIP database, 951 patients were identified with a 30-day unplanned reoperation rate of 12.3%. On multivariate analysis in the NSQIP database, cervical spine abscess was the only factor that reached significance for 30-day reoperation (OR=1.71, 95% CI=1.11–2.63, p=.02, Area under the curve (AUC)=0.61). On multivariate analysis in the institutional cohort, independent prognostic factors for 30-day reoperation were: preoperative urinary incontinence, ventral location of abscess relative to thecal sac, cervical abscess, preoperative wound infection, and leukocytosis (AUC=0.65). Ninety-day reoperation rate also found hypoalbuminemia as a significant predictor (AUC=0.66).CONCLUSIONSix novel independent prognostic factors were identified for 90-day reoperation after surgery for a spinal epidural abscess. The multivariable analysis fairly predicts reoperation, indicating that there may be additional factors that need to be uncovered in future studies. The risk factors delineated in this study through the use of two large cohorts of spinal epidural abscess patients can be used to improve preoperative risk stratification and patient management.     

Development of a machine learning algorithm for predicting in-hospital and 1-year mortality after traumatic spinal cord injury

BACKGROUND CONTEXTCurrent prognostic tools such as the Injury Severity Score (ISS) that predict mortality following trauma do not adequately consider the unique characteristics of traumatic spinal cord injury (tSCI).PURPOSEOur aim was to develop and validate a prognostic tool that can predict mortality following tSCI.STUDY DESIGNRetrospective review of a prospective cohort study.PATIENT SAMPLEData was collected from 1245 persons with acute tSCI who were enrolled in the Rick Hansen Spinal Cord Injury Registry between 2004 and 2016.OUTCOME MEASURESIn-hospital and 1-year mortality following tSCI.METHODSMachine learning techniques were used on patient-level data (n=849) to develop the Spinal Cord Injury Risk Score (SCIRS) that can predict mortality based on age, neurological level and completeness of injury, AOSpine classification of spinal column injury morphology, and Abbreviated Injury Scale scores. Validation of the SCIRS was performed by testing its accuracy in an independent validation cohort (n=396) and comparing its performance to the ISS, a measure which is used to predict mortality following general trauma.RESULTSFor 1-year mortality prediction, the values for the Area Under the Receiver Operating Characteristic Curve (AUC) for the development cohort were 0.84 (standard deviation=0.029) for the SCIRS and 0.55 (0.041) for the ISS. For the validation cohort, AUC values were 0.86 (0.051) for the SCIRS and 0.71 (0.074) for the ISS. For in-hospital mortality, AUC values for the development cohort were 0.87 (0.028) and 0.60 (0.050) for the SCIRS and ISS, respectively. For the validation cohort, AUC values were 0.85 (0.054) for the SCIRS and 0.70 (0.079) for the ISS.CONCLUSIONSThe SCIRS can predict in-hospital and 1-year mortality following tSCI more accurately than the ISS. The SCIRS can be used in research to reduce bias in estimating parameters and can help adjust for coefficients during model development. Further validation using larger sample sizes and independent datasets is needed to assess its reliability and to evaluate using it as an assessment tool to guide clinical decision-making and discussions with patients and families.     

External validation of the PRESTO pediatric tool for predicting in-hospital mortality from traumatic injury

BackgroundBenchmarking is crucial for quality improvement of trauma systems. The Pediatric Resuscitation and Trauma Outcome (PRESTO) model allows risk-adjusted comparisons of in-hospital mortality for pediatric trauma populations in under-resourced environments. Our aim was to validate PRESTO in a high-resource setting using provincial Trauma Registry (TR) data and compare it to the standard benchmarking model, the Injury Severity Score (ISS).MethodsThis retrospective case-control study collected demographic, vital sign, and outcome data from the TR for patients aged <16 years sustaining major trauma from 2013 to 2021. The PRESTO model estimates predicted probability of in-hospital mortality (Pm) using the age, heart rate, blood pressure, oxygen saturation, neurological status, and use of airway supplementation. PRESTO was assessed by comparison of Pm in patients who died and survived and comparison of area under the receiver–operator curve (AUROC) with that of ISS. Statistical analysis was performed using R.ResultsWe included 647 patients, of which 69 died in-hospital (11%). The cohort was 37% female, with a median age of 8 and median ISS of 17. The median Pm for cases was significantly higher compared to controls (1.0 vs. 5.2 × 10⁻⁵, p < 0.001). The AUROC for PRESTO and ISS were not significantly different (0.819 and 0.816, respectively; p = 0.95).ConclusionPRESTO is valid in a resource-rich environment, such as a Canadian province. It performs equally well to ISS but is simpler to derive. In the future, PRESTO may serve to benchmark levels of in-hospital mortality within or across institutions over time across Canada.Level of evidence3     

Long-term outcome after neurosurgically treated spinal epidural abscess following epidural analgesia

BACKGROUND: A recent investigation demonstrated a high incidence of epidural abscess secondary to epidural catheterization and a 50% frequency of neurologic deficits. We studied short- and long-term neurologic outcome in patients operated for spinal epidural abscess after epidural analgesia. METHODS: Nineteen patients who had undergone neurosurgical decompression and drainage of a spinal epidural abscess during a 5-year period at three neurosurgical departments in East Denmark were identified by manual review of operating lists. RESULTS: Median epidural catheterization time was 8 days (range 3-44). Preoperatively 12 patients suffered from inferior paraparesis, one had irradiating pain from the back, and 6 patients had no neurologic deficits. Postoperatively 2 patients had recovered, but 3 other patients had deteriorated; therefore, 13 patients were discharged with paresis/plegia. Seven patients died during a median follow-up time for all patients of 41.6 months. One patient recovered completely, and one suffered from minor deficits. The remaining patients suffered from paraparesis/plegia or bladder/bowel dysfunction. CONCLUSION: Overall recovery rate for patients with paresis/plegia after epidural abscess was 20%. No patients with paresis/plegia following a thoracic abscess recovered in contrast to a 50% recovery rate for patients with lumbar epidural abscess. The majority of long-term survivors had severe neurologic deficits. Abscess formation contributed to one death.     

External validation of the U-HIP prediction model for in-hospital mortality in geriatric hip fracture patients

IntroductionIdentification of high-risk hip fracture patients in an early stage is vital for guiding surgical management and shared decision making. To objective of this study was to perform an external international validation study of the U-HIP prediction model for in-hospital mortality in geriatric patients with a hip fracture undergoing surgery.Materials and methodsIn this retrospective cohort study, data were used from The American College of Surgeons National Surgical Quality Improvement Program. Patients aged 70 years or above undergoing hip fracture surgery were included. The discrimination (c-statistic) and calibration of the model were investigated.ResultsA total of 25,502 patients were included, of whom 618 (2.4%) died. The mean predicted probability of in-hospital mortality was 3.9% (range 0%-55%). The c-statistic of the model was 0.74 (95% CI 0.72–0.76), which was comparable to the c-statistic of 0.78 (95% CI 0.71–0.85) that was found in the development cohort. The calibration plot indicated that the model was slightly overfitted, with a calibration-in-the-large of 0.015 and a calibration slope of 0.780. Within the subgroup of patients aged between 70 and 85, however, the c-statistic was 0.78 (95% CI 0.75–0.81), with good calibration (calibration slope 0.934).Discussion and conclusionThe U-HIP model for in-hospital mortality in geriatric hip fractures was externally validated in a large international cohort, and showed a good discrimination and fair calibration. This model is freely available online and can be used to predict the risk of mortality, identify high-risk patients and aid clinical decision making.     

Infectious spondylodiskitis and epidural abscess after spinal puncture for pilonidal sinus excision

Vertebral infections after spinal puncture are rare and often inadequately documented. Their incidence does not exceed that of spontaneous epidural abscesses and we should therefore be cautious about assuming a causal relation between puncture and an abscess. After analyzing 10 published cases we saw that only half of them reported on aseptic conditions and only 2 patients seem to have had a prior infection. In 3 cases, the abscesses appeared after technically simple punctures whereas half the reports did not even mention the type of puncture. This complication should be considered whenever a patient develops back pain and fever, even if there are no neurological deficits and even after a simple spinal puncture. Given that early diagnosis and treatment have proven effective in improving the survival rate and reducing the rate of neurological sequelae, magnetic resonance images should be ordered urgently so that early treatment can be established.     

Association between interhospital transfer and increased in-hospital mortality in patients with spinal epidural abscesses

BACKGROUND CONTEXTSpinal epidural abscess (SEA) is an uncommon yet serious infection, associated with significant morbidity and mortality. Patients diagnosed with SEA often require surgical interventions or critical care services that are not available at community hospitals and are therefore transferred to tertiary care centers. Little is known about the effects of interhospital transfer on acute outcomes for patients with SEA.PURPOSETo study the effects of interhospital transfer on acute outcomes for patients with SEA.STUDY DESIGNCross sectional analysis using the 2009 to 2017 National Inpatient Sample (NIS).PATIENT SAMPLEUsing the 2009 to 2017 NIS, we identified cases of SEA using ICD, Ninth, or Tenth Revision diagnosis codes 324.1 & G06.1.OUTCOME MEASURESOur primary endpoint was in hospital mortality.METHODSThe association between interhospital transfer and inpatient mortality was assessed using multivariable logistic regression to adjust for potential covariates. Patient and hospital factors associated with interhospital transfer were assessed in a secondary analysis.RESULTSA total of 21.5% of patient with SEA were treated after transfer from another hospital. After adjusting for covariates, those who presented after transfer had higher odds of death during hospitalization (OR: 1.51, 95% CI 1.27–1.78, p<.001). Transferred patients were significantly more likely to live in rural communities (11.4 % vs. 5.3 % for nontransferred patients).CONCLUSIONSInterhospital transfer, which occurred more frequently in patients from rural hospitals, was associated with death even after controlling for disease severity. Addressing healthcare delivery disparities across the US, including across the rural-urban spectrum, will require better understanding of the observed increased mortality of interhospital transfer as a preventable source of in-hospital mortality for SEA.     

Predicting in-hospital mortality after traumatic brain injury: External validation of CRASH-basic and IMPACT-core in the national trauma data bank

BackgroundTraumatic brain injury (TBI) prognostic prediction models offer value to individualized treatment planning, systematic outcome assessments and clinical research design but require continuous external validation to ensure generalizability to different settings. The Corticosteroid Randomization After Significant Head Injury (CRASH) and International Mission on Prognosis and Analysis on Clinical Trials in TBI (IMPACT) models are widely available but lack robust assessments of performance in a current national sample of patients. The purpose of this study is to assess the performance of the CRASH-Basic and IMPACT-Core models in predicting in-hospital mortality using a nationwide retrospective cohort from the National Trauma Data Bank (NTDB).MethodsThe 2016 NTDB was used to analyze an adult cohort with moderate-severe TBI (Glasgow Coma Scale [GCS] ≤ 12, head Abbreviated Injury Scale of 2–6). Observed in-hospital mortality or discharge to hospice was compared to the CRASH-Basic and IMPACT-Core models’ predicted probability of 14-day or 6-month mortality, respectively. Performance measures included discrimination (area under the receiver operating characteristic curve [AUC]) and calibration (calibration plots and Brier scores). Further sensitivity analysis included patients with GCS ≤ 14 and considered patients discharged to hospice to be alive at 14-days.ResultsA total of 26,228 patients were included in this study. Both models demonstrated good ability in differentiating between patients who died and those who survived, with IMPACT demonstrating a marginally greater AUC (0.863; 95% CI: 0.858 – 0.867) than CRASH (0.858; 0.854 – 0.863); p < 0.001. On calibration, IMPACT overpredicted at lower scores and underpredicted at higher scores but had good calibration-in-the-large (indicating no systemic over/underprediction), while CRASH consistently underpredicted mortality. Brier scores were similar (0.152 for IMPACT, 0.162 for CRASH; p < 0.001). Both models showed slight improvement in performance when including patients with GCS ≤ 14.ConclusionBoth CRASH-Basic and IMPACT-Core accurately predict in-hospital mortality following moderate-severe TBI, and IMPACT-Core performs well beyond its original GCS cut-off of 12, indicating potential utility for mild TBI (GCS 13–15). By demonstrating validity in the NTDB, these models appear generalizable to new data and offer value to current practice in diverse settings as well as to large-scale research design.Introduction     

Sarcopenia predicts 90-day mortality and postoperative complications after radical cystectomy for bladder cancer

Purpose

A single-center study was conducted to investigate the impact of sarcopenia as a predictor for 90-day mortality (90 dM) and complications within 90 days after radical cystectomy for bladder cancer.

Methods

In total, 327 patients with preoperative available digital computed tomography (CT) scans of the abdomen and pelvis were identified. The lumbar skeletal muscle index was measured using preoperative abdominal CT to assess sarcopenia. Complications were recorded and graded according to Clavien–Dindo (CD). Predictors of 90 dM and complications within 90 days were analyzed by uni- and multivariable logistic regression.

Results

Of the 327 patients, 262 (80%) were male and 108 (33%) patients were classified as sarcopenic. Within 90 days, 28 (7.8%) patients died, of whom 15 patients were sarcopenic and 13 were not. In multivariable logistic regression analysis, sarcopenia (OR 2.59; 95% CI 1.13–5.95; p?=?0.025), ASA 3–4 (OR 2.53; 95% CI 1.10–5.82; p?=?0.029) and cM?+?(OR 7.43; 95% CI 2.34–23.64; p?=?0.001) were independent predictors of 90-day mortality. Sarcopenic patients experienced significantly more complications, i.e., CD 4a–5 (p?=?0.003), compared to non-sarcopenic patients. In multivariable logistic regression analysis, sarcopenia was independently associated with CD?≥?3b complications corrected for age, BMI, ASA-Score and type of urinary diversion.

Conclusions

We reported that sarcopenia proved an independent predictor for 90 dM and complications in patients undergoing RC for bladder cancer.

     

Predicting 90-day mortality after bariatric surgery: an independent,external validation of the OS-MRS prognostic risk score

BackgroundThe Obesity Surgery Mortality Risk Score (OS-MRS) was developed using data from 1995 to 2004; it has yet to be validated for more recent patients in integrated delivery system settings. The objective of this study was to validate the OS-MRS using data from electronic health records in a distributed data network.MethodsWe conducted a retrospective cohort study of 3,817 adults who underwent an open (21.4%) or laparoscopic (78.6%) gastric bypass surgery between 2005 and 2007 in the Scalable Partnering Network. Our main outcome was all-cause mortality during the 90 days after surgery. We scored patients’ risk of mortality by adding characteristics according to the OS-MRS (i.e., 1 point for each predictor).ResultsSixteen of 3,817 (0.42/100; 95% CI, .24–.68) patients died within 90 days. The OS-MRS discriminated low-risk and high-risk patients effectively: low-risk (2 of 1,654 patients; .12 deaths/100 patients), intermediate-risk (10 of 2,008 patients; .50 deaths/100 patients), and high-risk (4 of 155 patients; 2.58 deaths/100 patients). High-risk patients were 21.3 times more likely to die in the first 90 days after surgery than low-risk patients (risk ratio = 21.3; 95% CI, 3.9–115.6).ConclusionIn these 10 U.S. healthcare delivery systems, the OS-MRS appears valid—albeit with the caveat that we observed a small number of deaths. The OS-MRS appears useful for identifying the small fraction of patients at high risk for 90-day mortality after open and laparoscopic RYGB.     

Vertebral osteomyelitis and epidural abscess after epidural anesthesia for a cesarean section

A 40-year-old woman underwent cesarean section under epidural anesthesia. The anesthetic procedure was carried out in strict aseptic conditions, the catheter was withdrawn 24 hours after surgery, and the patient was discharged 5 days after surgery. She was readmitted with fever, backache, and pain in the lower limbs, with signs of radiculitis but no indication of inflammation or pain at the site of puncture. Magnetic resonance imaging revealed vertebral osteomyelitis at the fifth lumbar and first sacral vertebrae and an epidural abscess with compression of the nerve root. Treatment consisted of 2 g of ceftriaxone daily for 6 weeks, rest, and measures to assure local immobilization. Symptoms gradually improved and no surgical drainage measures were needed. The cause of osteomyelitis was never ascertained. Vertebral osteomyelitis is an unusual event after epidural anesthesia and there have been few opportunities to demonstrate a relationship. Such infections appear spontaneously in immunodepressed patients who undergo diagnostic procedures and treatments that lead to bacteremias with secondary colonization of spinal structures. The topography and characteristics of the infectious lesion, the patient's susceptibility, and the anesthetic procedure and pathogenic agent may help clarify the cause of the osteomyelitis.