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Objectives: Distal radius malunion can result in pain and functional complaints. One of the functional problems that can affect daily life is impaired forearm rotation. The primary aim of this study was to investigate the effect of corrective osteotomy for distal radius malunion on forearm rotation at 12 months after surgery. We secondarily studied the effect on grip strength, radiological measurements, and patient-reported outcome measurements (PROMs). Patients and methods: This cohort study analysed prospectively collected data of adult patients with symptomatic distal radius malunion. All patients underwent corrective osteotomy for malunion and were followed for 1 year. We measured forearm rotation (pronation and supination) and grip strength and analysed radiographs. PROMs consisted of the Patient-Rated Hand/Wrist Evaluation (PRWHE) questionnaire, Visual Analogue Scale for pain, and satisfaction with hand function. Results:Preoperative total forearm rotation was 112° (SD: 34°), of which supination of 49° (SD: 25°) was more impaired than pronation of 63° (SD: 17°). Twelve months after surgery, an unpaired Student's t-test showed a significant improvement of total forearm rotation to 142° (SD: 17°) (p < 0.05). Pronation improved to 72° (SD: 10°), and supination to 69° (SD: 13°) (p < 0.05). Grip strength, PROMs, as well as inclination and volar tilt on radiographs improved significantly during the first year after surgery (p < 0.05). Conclusion: In patients with reduced forearm rotation due to distal radius malunion, corrective osteotomy is an effective treatment that significantly improves forearm rotation. In addition, this intervention improves grip strength, the PRWHE-score, pain, and satisfaction with hand function.

Background Previous studies using patient-reported outcomes measures (PROMs) to monitor symptoms during and after (lung) cancer treatment used alerts that were sent to the health-care provider, although an approach in which patients receive alerts could be more clinically feasible. The primary aim of this study was to compare the effect of weekly PROM symptom monitoring via a reactive approach (patient receives alert) or active approach (health-care provider receives alert) with care as usual on health-related quality of life (HRQOL) at 15 weeks after start of treatment in lung cancer patients. Methods The SYMPRO–Lung trial is a multicenter randomized controlled trial using a stepped wedge design. Stage I-IV lung cancer patients in the reactive and active groups reported PROM symptoms weekly, which were linked to a common alerting algorithm. HRQOL was measured by the EORTC QLQ-C30 at baseline and after 15 weeks. Linear regression analyses and effect size estimates were used to assess mean QOL–C30 change scores between groups, accounting for confounding. Results A total of 515 patients were included (160 active group, 89 reactive group, 266 control group). No differences in HRQOL were observed between the reactive and active group (summary score: unstandardized beta [B] = 0.51, 95% confidence interval [CI] = -3.22 to 4.24, Cohen d effect size [ES] = 0.06; physical functioning: B = 0.25, 95% CI = -5.15 to 4.64, ES = 0.02). The combined intervention groups had statistically and clinically significantly better mean change scores on the summary score (B = 4.85, 95% CI = 1.96 to 7.73, ES = 0.57) and physical functioning (B = 7.00, 95% CI = 2.90 to 11.09, ES = 0.71) compared with the control group. Conclusions Weekly PRO symptom monitoring statistically and clinically significantly improves HRQOL in lung cancer patients. The logistically less intensive, reactive approach may be a better fit for implementation.

This scoping review focuses on the essential role of models for causal inference in shaping actionable artificial intelligence (AI) designed to aid clinicians in decision-making. The objective was to identify and evaluate the reporting quality of studies introducing models for causal inference in intensive care units (ICUs), and to provide recommendations to improve the future landscape of research practices in this domain. To achieve this, we searched various databases including Embase, MEDLINE ALL, Web of Science Core Collection, Google Scholar, medRxiv, bioRxiv, arXiv, and the ACM Digital Library. Studies involving models for causal inference addressing time-varying treatments in the adult ICU were reviewed. Data extraction encompassed the study settings and methodologies applied. Furthermore, we assessed reporting quality of target trial components (i.e., eligibility criteria, treatment strategies, follow-up period, outcome, and analysis plan) and main causal assumptions (i.e., conditional exchangeability, positivity, and consistency). Among the 2184 titles screened, 79 studies met the inclusion criteria. The methodologies used were G methods (61%) and reinforcement learning methods (39%). Studies considered both static (51%) and dynamic treatment regimes (49%). Only 30 (38%) of the studies reported all five target trial components, and only seven (9%) studies mentioned all three causal assumptions. To achieve actionable AI in the ICU, we advocate careful consideration of the causal question of interest, describing this research question as a target trial emulation, usage of appropriate causal inference methods, and acknowledgement (and examination of potential violations of) the causal assumptions.

Background: The COVID-19 pandemic continues to overwhelm intensive care units (ICUs) worldwide, and improved prediction of mortality among COVID-19 patients could assist decision making in the ICU setting. In this work, we report on the development and validation of a dynamic mortality model specifically for critically ill COVID-19 patients and discuss its potential utility in the ICU. Methods: We collected electronic medical record (EMR) data from 3222 ICU admissions with a COVID-19 infection from 25 different ICUs in the Netherlands. We extracted daily observations of each patient and fitted both a linear (logistic regression) and non-linear (random forest) model to predict mortality within 24 h from the moment of prediction. Isotonic regression was used to re-calibrate the predictions of the fitted models. We evaluated the models in a leave-one-ICU-out (LOIO) cross-validation procedure. Results: The logistic regression and random forest model yielded an area under the receiver operating characteristic curve of 0.87 [0.85; 0.88] and 0.86 [0.84; 0.88], respectively. The recalibrated model predictions showed a calibration intercept of −0.04 [−0.12; 0.04] and slope of 0.90 [0.85; 0.95] for logistic regression model and a calibration intercept of −0.19 [−0.27; −0.10] and slope of 0.89 [0.84; 0.94] for the random forest model. Discussion: We presented a model for dynamic mortality prediction, specifically for critically ill COVID-19 patients, which predicts near-term mortality rather than in-ICU mortality. The potential clinical utility of dynamic mortality models such as benchmarking, improving resource allocation and informing family members, as well as the development of models with more causal structure, should be topics for future research.

Pattern Recognition and Bioinformatics