ABSTRACT
STUDY DESIGN: Retrospective cohort study. OBJECTIVES: Minimizing delays in referral, diagnosis and treatment of patients with symptomatic spinal metastases is important for optimal treatment outcomes. The primary objective of this study was to investigate several forms of delay from the onset of symptoms until surgical treatment of spinal metastases for patients with and without a known preexisting known malignancy. METHODS: All patients receiving surgical treatment for spinal metastases in a single tertiary spine center were identified. Referral patterns were reconstructed and the total delay was divided into 4 categories: patient delay (onset of symptoms until medical consultation), diagnostic delay (medical consultation until diagnosis), referral delay (diagnosis until referral to spine surgeon) and treatment delay (referral spine to surgeon until treatment). These intervals were compared between patients with and without a known preexisting malignancy. RESULTS: The median total delay was 99 days, patient delay 19 days, diagnostic delay 21,5 days, referral delay 7 days, treatment delay 8 days and diagnosis and treatment delay combined 18,5 days. No difference in total delay was observed between patients with and without a known preexisting malignancy. Total delay was not significantly associated with patient age, sex, oncological history, tumor prognosis and spinal level of the tumor. CONCLUSIONS: Patients with symptomatic spinal metastases experience considerable delays, even after metastatic spinal disease has been diagnosed, regardless of a preexisting malignancy. By identifying and eliminating the causes of these delays, diagnosis, referral and treatment may be expedited leading to improved patient outcome.
ABSTRACT
PURPOSE: Pain response after conventional external beam radiation therapy (cRT) in patients with painful bone metastases is observed in 60% to 70% of patients. The aim of the VERTICAL trial was to investigate whether stereotactic body radiation therapy (SBRT) improves pain response. METHODS AND MATERIALS: This single-center, phase 2, randomized controlled trial was conducted within the PRESENT cohort, which consists of patients referred for radiation therapy of bone metastases to our tertiary center. Cohort participants with painful bone metastases who gave broad informed consent for randomization were randomly assigned to cRT or SBRT. Only patients in the intervention arm received information about the trial and were offered SBRT (1 × 18 Gy, 3 × 10 Gy, or 5 × 7 Gy), which they could accept or refuse. Patients who refused SBRT underwent standard cRT (1 × 8 Gy, 5 × 4 Gy, or 10 × 3 Gy). Patients in the control arm were not informed. Primary endpoint was pain response at 3 months after radiation therapy. Secondary outcomes were pain response at any point within 3 months, mean pain scores, and toxicity. Data were analyzed intention to treat (ITT) and per protocol (PP). This trial was registered with Clinicaltrials.gov, NCT02364115. RESULTS: Between January 29, 2015, and March 20, 2019, 110 patients were randomized. ITT analysis included 44 patients in the cRT arm and 45 patients in the SBRT arm. In the intervention arm, 12 patients (27%) declined SBRT, and 7 patients (16%) were unable to complete the SBRT treatment. In ITT, 14 of 44 patients (32%; 95% confidence interval [CI], 18%-45%) in the control arm and 18 of 45 patients (40%; 95% CI, 26%-54%) in the SBRT arm reported a pain response at 3 months (P = .42). In PP, these proportions were 14 of 44 (32%; 95% CI, 18%-45%) and 12 of 23 patients (46%; 95% CI, 27%-66%), respectively (P = .55). In ITT, a pain response within 3 months was reported by 30 of 44 control patients (82%; 95% CI, 68%-90%) and 38 of 45 patients (84%; 95% CI, 71%-92%) in the SBRT arm (P = .12). In PP, these proportions were 36 of 44 (82%; 95% CI, 68%-90%) and 26 of 27 patients (96%; 95% CI; 81%-100%), respectively (P = .12). No grade 3 or 4 toxicity was observed in either arm. CONCLUSIONS: SBRT did not significantly improve pain response in patients with painful bone metastases. One in 4 patients preferred to undergo cRT over SBRT, and 1 in 5 patients starting SBRT was unable to complete this treatment. Because of this selective dropout, which can be attributed to the character of the intervention, the trial was underpowered to detect the prespecified difference in pain response.
Subject(s)
Bone Neoplasms/radiotherapy , Cancer Pain/radiotherapy , Radiosurgery/methods , Aged , Bone Neoplasms/mortality , Bone Neoplasms/secondary , Cancer Pain/mortality , Confidence Intervals , Dose Fractionation, Radiation , Female , Health Surveys/statistics & numerical data , Humans , Male , Middle Aged , Pain Measurement , Patient Dropouts/statistics & numerical data , Prospective Studies , Radiosurgery/statistics & numerical data , Radiotherapy/statistics & numerical data , Radiotherapy Dosage , Radiotherapy Planning, Computer-Assisted , Spinal Neoplasms/radiotherapy , Time Factors , Treatment OutcomeABSTRACT
PURPOSE: An excessive amount of total hospitalization is caused by delays due to patients waiting to be placed in a rehabilitation facility or skilled nursing facility (RF/SNF). An accurate preoperative prediction of who would need a RF/SNF place after surgery could reduce costs and allow more efficient organizational planning. We aimed to develop a machine learning algorithm that predicts non-home discharge after elective surgery for lumbar spinal stenosis. METHODS: We used the American College of Surgeons National Surgical Quality Improvement Program to select patient that underwent elective surgery for lumbar spinal stenosis between 2009 and 2016. The primary outcome measure for the algorithm was non-home discharge. Four machine learning algorithms were developed to predict non-home discharge. Performance of the algorithms was measured with discrimination, calibration, and an overall performance score. RESULTS: We included 28,600 patients with a median age of 67 (interquartile range 58-74). The non-home discharge rate was 18.2%. Our final model consisted of the following variables: age, sex, body mass index, diabetes, functional status, ASA class, level, fusion, preoperative hematocrit, and preoperative serum creatinine. The neural network was the best model based on discrimination (c-statistic = 0.751), calibration (slope = 0.933; intercept = 0.037), and overall performance (Brier score = 0.131). CONCLUSIONS: A machine learning algorithm is able to predict discharge placement after surgery for lumbar spinal stenosis with both good discrimination and calibration. Implementing this type of algorithm in clinical practice could avert risks associated with delayed discharge and lower costs. These slides can be retrieved under Electronic Supplementary Material.
Subject(s)
Lumbar Vertebrae/surgery , Machine Learning , Patient Discharge , Postoperative Care/methods , Spinal Stenosis/surgery , Aged , Algorithms , Elective Surgical Procedures , Female , Humans , Male , Middle Aged , Netherlands , Neural Networks, Computer , Patient Transfer/organization & administration , Predictive Value of Tests , Quality Improvement , Rehabilitation Centers , Skilled Nursing FacilitiesABSTRACT
BACKGROUND: In metastatic renal cell carcinoma (mRCC) there has been a treatment shift towards targeted therapy, which has resulted in improved overall survival. Therefore, there is a need for better local control of the tumor and its metastases. Image-guided stereotactic body radiotherapy (SBRT) in bone metastases provides improved symptom palliation and local control. With the use of SBRT there is a need for accurate target delineation. The hypothesis is that MRI allows for better visualization of the extend of bone metastases in mRCC and will optimize the accuracy of tumor delineation for stereotactic radiotherapy purposes, compared with CT only. METHODS: From 2013 to 2016, patients who underwent SBRT for RCC bone metastases were included. A planning CT and MRI were performed in radiotherapy treatment position. Gross tumor volumes (GTV) in both CT and MRI were delineated. Contouring was performed by a radiation oncologist specialized in bone metastases and verified by a radiologist, based on local consensus contouring guidelines. In both CT and MRI, the GTV volumes, conformity index (CI) and distance between the centers of mass (dCOM) were compared. RESULTS: Nine patients with 11 RCC bone metastases were included. The GTV volume as defined on MRI was in all cases larger or at least as large as the GTV volume on CT. The median GTV volume on MRI was 33.4 mL (range 0.2-247.6 mL), compared to 18.1 mL on CT (range 0.1-195.9) (P=0.013). CONCLUSIONS: Contouring of RCC bone metastases on MRI resulted in clinically relevant and statistically significant larger lesions (mean increase 41%) compared with CT. MRI seems to represent the extend of the GTV in RCC bone metastases more accurately. Contouring based on CT-only could result in an underestimation of the actual tumor volume, which may cause underdosage of the GTV in SBRT treatment plans.