Prognosis Research Strategy (PROGRESS) 3: prognostic model research

Validating the ratio of insulin like growth factor binding protein 4 to sex hormone binding globulin as a prognostic predictor of preterm birth in Viet Nam: a case-cohort study

OBJECTIVE

To validate a serum biomarker developed in the USA for preterm birth (PTB) risk stratification in Viet Nam.

METHODS

Women with singleton pregnancies (n = 5000) were recruited between 19+0-23+6 weeks' gestation at Tu Du Hospital, Ho Chi Minh City. Maternal serum was collected from 19+0-22+6 weeks' gestation and participants followed to neonatal discharge. Relative insulin-like growth factor binding protein 4 (IGFBP4) and sex hormone binding globulin (SHBG) abundances were measured by mass spectrometry and their ratio compared between PTB cases and term controls. Discrimination (area under the receiver operating characteristic curve, AUC) and calibration for PTB <37 and <34 weeks' gestation were tested, with model tuning using clinical factors. Measured outcomes included all PTBs (any birth ≤37 weeks' gestation) and spontaneous PTBs (birth ≤37 weeks' gestation with clinical signs of initiation of parturition).

RESULTS

Complete data were available for 4984 (99.7%) individuals. The cohort PTB rate was 6.7% (n = 335). We observed an inverse association between the IGFBP4/SHBG ratio and gestational age at birth (p = 0.017; AUC 0.60 [95% CI, 0.53-0.68]). Including previous PTB (for multiparous women) or prior miscarriage (for primiparous women) improved performance (AUC 0.65 and 0.70, respectively, for PTB <37 and <34 weeks' gestation). Optimal performance (AUC 0.74) was seen within 19-20 weeks' gestation, for BMI >21 kg/m2 and age 20-35 years.

CONCLUSION

We have validated a novel serum biomarker for PTB risk stratification in a very different setting to the original study. Further research is required to determine appropriate ratio thresholds based on the prevalence of risk factors and the availability of resources and preventative therapies.

Multivariable prediction models for fetal macrosomia and large for gestational age: A systematic review

BACKGROUND

The identification of large for gestational age (LGA) and macrosomic fetuses is essential for counselling and managing these pregnancies.

OBJECTIVES

To systematically review the literature for multivariable prediction models for LGA and macrosomia, assessing the performance, quality and applicability of the included model in clinical practice.

SEARCH STRATEGY

MEDLINE, EMBASE and Cochrane Library were searched until June 2022.

SELECTION CRITERIA

We included observational and experimental studies reporting the development and/or validation of any multivariable prediction model for fetal macrosomia and/or LGA. We excluded studies that used a single variable or did not evaluate model performance.

DATA COLLECTION AND ANALYSIS

Data were extracted using the Checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies checklist. The model performance measures discrimination, calibration and validation were extracted. The quality and completion of reporting within each study was assessed by its adherence to the TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis) checklist. The risk of bias and applicability were measured using PROBAST (Prediction model Risk Of Bias Assessment Tool).

MAIN RESULTS

A total of 8442 citations were identified, with 58 included in the analysis: 32/58 (55.2%) developed, 21/58 (36.2%) developed and internally validated and 2/58 (3.4%) developed and externally validated a model. Only three studies externally validated pre-existing models. Macrosomia and LGA were differentially defined by many studies. In total, 111 multivariable prediction models were developed using 112 different variables. Model discrimination was wide ranging area under the receiver operating characteristics curve (AUROC 0.56-0.96) and few studies reported calibration (11/58, 19.0%). Only 5/58 (8.6%) studies had a low risk of bias.

CONCLUSIONS

There are currently no multivariable prediction models for macrosomia/LGA that are ready for clinical implementation.

Child maltreatment elevated the risk of late-life chronic pain: a biopsychosocial framework from the UK Biobank cohort

ABSTRACT

Understanding the development of chronic pain (CP) is challenging due to its multifactorial etiology. Child maltreatment (CM), encompassing various types of neglect and abuse affecting more than one-third of the population, is a critical aspect of early-life adversity with long-lasting impacts. It is increasingly recognized for its role in altering biopsychosocial processes, potentially increasing vulnerability to CP. However, the exact path connecting CM to CP is not fully elucidated, primarily attributable to limitations in prior research, including insufficient sample sizes, inadequate consideration of comprehensive mediative variables, and a lack of longitudinal data. To address these gaps, our study utilizes a large-scale dataset (n = 150,989) comprising both cross-sectional and longitudinal data, along with an extensive range of biopsychosocial variables. Our findings reveal that all types of CMs, except physical neglect, significantly increase the risk of CP, and all types of CPs, except headache, were affected by CM. Furthermore, we demonstrate that individuals with CM histories are more predisposed to comorbid CP conditions. Importantly, biopsychosocial factors are found to explain over 60% of the association between CM and CP, with psychological factors playing a key role. This study not only characterizes the relationship between CM and CP but also underscores the influence of psychosocial elements in this dynamic interplay. These findings offer important insights into the long-term impacts of CM and provide a foundation for developing targeted therapeutic and preventive strategies for CP.

Prognostic factors of pain, disability, and poor outcomes in persons with neck pain - an umbrella review

OBJECTIVE

The aim of this study was to identify prognostic factors pertaining to neck pain from systematic reviews.

DATA SOURCES

A search on PubMed, Scopus, and CINAHL was performed on June 27, 2024. Additional grey literature searches were performed.

REVIEW METHODS

We conducted an umbrella review and included systematic reviews reporting the prognostic factors associated with non-specific or trauma-related neck pain and cervical radiculopathy. Prognostic factors were sorted according to the outcome predicted, the direction of the predicted outcome (worse, better, inconsistent), and the grade of evidence (Oxford Center of Evidence). The predicted outcomes were regrouped into five categories: pain, disability, work-related outcomes, quality of life, and poor outcomes (as "recovery"). Risk of bias analysis was performed with the ROBIS tool.

RESULTS

We retrieved 884 citations from three databases, read 39 full texts, and included 16 studies that met all selection criteria. From these studies, we extracted 44 prognostic factors restricted to non-specific neck pain, 47 for trauma-related neck pain, and one for cervical radiculopathy. We observed that among the prognostic factors, most were associated with characteristics of the condition, cognitive-emotional factors, or socio-environmental and lifestyle factors.

CONCLUSION

This study identified over 40 prognostic factors associated mainly with non-specific neck pain or trauma-related neck pain. We found that a majority were associated with worse outcomes and pertained to domains mainly involving cognitive-emotional factors, socio-environmental and lifestyle factors, and the characteristics of the condition to predict outcomes and potentially guide clinicians to tailor their interventions for people living with neck pain.

Predicting the resolution of hypertension following adrenalectomy in primary aldosteronism: Controversies and unresolved issues a narrative review

BACKGROUND

Hypertension resolution following adrenalectomy in patients with primary aldosteronism (PA) remains a critical clinical challenge. Identifying preoperatively which patients will become normotensive is both a priority and a point of contention. In this narrative review, we explore the controversies and unresolved issues surrounding the prediction of hypertension resolution after adrenalectomy in PA.

METHODS

A comprehensive literature review was conducted, focusing on studies published between 1954 and 2024 that evaluated all studies that discussed predictive models for hypertension resolution post-adrenalectomy in PA patients. Databases searched included MEDLINE®, Ovid Embase, and Web of Science databases.

RESULTS

The review identified several predictors and predictive models of hypertension resolution, including female sex, duration of hypertension, antihypertensive medication, and BMI. However, inconsistencies in study designs and patient populations led to varied conclusions.

CONCLUSIONS

Although certain predictors and predictive models of hypertension resolution post-adrenalectomy in PA patients are supported by evidence, significant controversies and unresolved issues remain. While the current predictive models provide valuable insights, there is a clear need for further research in this area. Future studies should focus on validating and refining these models.

Development and Validation of Nosocomial Bacterial Infection Prediction Models for Patients With Systemic Lupus Erythematosus

BACKGROUND

Hospital-acquired bacterial infections are associated with high morbidity and mortality rates in patients with systemic lupus erythematosus (SLE). This study aimed to develop and validate predictive models for the risk of hospital-acquired bacterial infections in patients with SLE.

METHODS

A historical cohort study was designed for development, and another bidirectional cohort study was used for external validation. The risk of bacterial infection was assessed upon admission and after 5 days of hospitalization. Predictor selection employed the least absolute shrinkage and selection operator (LASSO) techniques. Multiple imputations were used to handle missing data. Logistic regression models were applied, and the properties of discrimination, calibration, and decision curve analysis were evaluated.

RESULTS

The development cohort comprised 1686 patients and 237 events (14.1%) from 3 tertiary hospitals. The external validation cohort included 531 patients and 84 infection outcomes (15.8%) from 10 hospital centers in Colombia (secondary and tertiary level). The models applied at admission and after 120 hours of stay exhibited good discrimination (AUC > 0.74). External validation demonstrated good performance among patients from the same tertiary institutions where the models were developed. However, geographic validation at other institutions has been suboptimal.

CONCLUSIONS

Two predictive models for nosocomial bacterial infections in patients with SLE are presented. All infection prevention recommendations should be maximized in patients at moderate/high risk. Further validation studies in diverse contexts, as well as clinical impact trials, are necessary before potential applications in research and clinical care.

Short-term mortality and associated factors among older hospitalized patients: A narrative retrospective analysis of end-of-life care in an acute geriatric unit

OBJECTIVES

To explore short-term mortality and its predictors among older patients hospitalized in a acute geriatric ward (AGW) in Northwestern Italy.

DESIGN

Retrospective observational single-center cohort study.

MATERIAL AND METHODS

Patients consecutively admitted for any reason between June 2021 and May 2022 were included in the analysis. Along with sociodemographic, clinical, and functional variables, prognosis estimation (Palliative Prognostic Index; PPI) at the time of admission was registered. Short-term all-cause mortality (in-hospital and within 3 months of discharge) was the primary outcome.

RESULTS

About one-third of the total sample died in the short-term (32.4 %). Along with PPI score (OR 1.115, 95 %CI 1.034-1.202), short-term mortality was independently associated with functional dependency (OR 1.278, 95 %CI 1.170-1.395).

CONCLUSIONS

The high short-term mortality in our sample should call for the inclusion of palliative prognostic tools within the in-hospital comprehensive geriatric assessment to better recognize and appropriately manage older patients at the end of life.

Prognostic model development for risk of curve progression in adolescent idiopathic scoliosis: a prospective cohort study of 127 patients

BACKGROUND AND PURPOSE

 The study's purpose was to develop and internally validate a prognostic survival model exploring baseline variables for adolescent idiopathic scoliosis curve progression.

METHODS

 A longitudinal prognostic cohort analysis was performed on trial data (n = 135) including girls and boys, Cobb angle 25-40°, aged 9-17 years, remaining growth > 1 year, and previously untreated. Prognostic outcome was defined as curve progression of Cobb angle of > 6° prior to skeletal maturity. 34 candidate prognostic variables were tested. Time-to-event was measured with 6-month intervals. Cox proportional hazards regression survival model (CoxPH) was used for model development and validation in comparison with machine learning models (66.6/33.3 train/test data set). The models were adjusted for treatment exposure.

RESULTS

 The final primary prognostic model included 127 patients, predicting progress with acceptable discriminative ability (concordance = 0.79, 95% confidence interval [CI] 0.72-0.86). Significant prognostic risk factors were Risser stage of 0 (HR 4.6, CI 2.1-10.1, P < 0.001), larger major curve Cobb angle (HRstandardized 1.5, CI 1.1-2.0, P = 0.005), and higher score on patient-reported pictorial Spinal Appearance Questionnaire (pSAQ) (HRstandardized 1.4, CI 1.0-1.9, P = 0.04). Treatment exposure, entered as a covariate adjustment, contributed significantly to the final model (HR 3.1, CI 1.5-6.0, P = 0.001). Sensitivity analysis displayed that CoxPH maintained acceptable discriminative ability (AUC 0.79, CI 0.65-0.93) in comparison with machine learning algorithms.

CONCLUSION

 The prognostic model (Risser stage, Cobb angle, pSAQ, and menarche) predicted curve progression of > 6° Cobb angle with acceptable discriminative ability. Adding patient report of the pSAQ may be of clinical importance for the prognosis of curve progression.

Can assessment of human assumed central sensitisation improve the predictive accuracy of the STarT Back screening tool in acute low back pain?

BACKGROUND

The STarT Back Screening Tool (SBT) is recommended to provide risk-stratified care in low back pain (LBP), yet its predictive value is moderate for disability and low for pain severity. Assessment of human assumed central sensitisation (HACS) in conjunction with the SBT may improve its predictive accuracy.

OBJECTIVES

To examine whether assessment of HACS in acute LBP improves the predictive accuracy of the SBT for LBP recovery at six months in people with acute non-specific LBP.

DESIGN

A prospective longitudinal study.

METHOD

Data were drawn from the UPWaRD study. One hundred and twenty people with acute non-specific LBP were recruited from the community. Baseline measures included SBT risk status, nociceptive flexor withdrawal reflex, pressure and heat pain thresholds and conditioned pain modulation. Primary outcome was the presence of LBP (pain numeric rating scale ≥1 and Roland Morris Disability Questionnaire score ≥3) at six-month follow-up. Regression coefficients were penalised using the least absolute shrinkage and selection operator technique to select predictor variables. Internal validation was performed using ten-fold cross-validation.

RESULTS/FINDINGS

SBT risk status alone did not predict the presence of LBP at six months (area under receiver operating characteristic curve [AUC] = 0.58). Adding measures of HACS to the SBT did not improve discrimination for whether LBP was present at six months (AUC = 0.59).

CONCLUSIONS

This study confirmed the suboptimal predictive accuracy of the SBT, administered during acute LBP, for LBP recovery at six months. Assessment of HACS in acute LBP does not improve the predictive accuracy of the SBT.

Development of prediction models based on risk scores for clinically significant prostate cancer on MRI/TRUS fusion biopsy

BACKGROUND

The implementation of population screening for prostate cancer has increased the number of patients with biochemical suspicion. Prediction models may reduce the number of unnecessary biopsies by identifying patients who benefit the most from them. Our aim is to develop a prediction model that is easily applicable in patients with suspicion of prostate cancer in the urology clinic setting to avoid unnecessary biopsies.

METHODS

We developed prediction models based on risk scores for the detection of prostate cancer and clinically significant prostate cancer using the TRIPOD guidelines. For this, we conducted an observational and retrospective review of computerised medical records of 204 patients undergoing prostate fusion biopsy between 2018 and 2021. We also reviewed other prediction models for prostate cancer including radiological parameters and targeted sampling of suspicious lesions.

RESULTS

A total of 204 patients underwent a biopsy, 138 were diagnosed of prostate cancer, and from them, 60 of clinically significant prostate cancer. Multivariate regression and random forest analysis were performed. Age, PSA density, diameter of the index lesions and PIRADS score on MRI were identified as predictors with an Area Under the Curve ranging between 0.71 and 0.80 and acceptable calibration results. Risk scores may avoid between 21.7% and 48.1% of biopsies.

CONCLUSION

Our prediction models are characterised by ease of use and may reduce unnecessary biopsies with satisfactory discrimination and calibration results while bringing benefits to the healthcare system and patients.

Prognostic factors associated with venous thromboembolism following traumatic injury: A systematic review and meta-analysis

INTRODUCTION

Trauma patients are at increased risk of venous thromboembolism (VTE), including deep venous thrombosis and/or pulmonary embolism. We conducted a systematic review and meta-analysis summarizing the association between prognostic factors and the occurrence of VTE following traumatic injury.

METHODS

We searched the Embase and Medline databases from inception to August 2023. We identified studies reporting confounding adjusted associations between patient, injury, or postinjury care factors and risk of VTE. We performed meta-analyses of odds ratios using the random-effects method and assessed individual study risk of bias using the Quality in Prognosis Studies tool.

RESULTS

We included 31 studies involving 1,981,946 patients. Studies were predominantly observational cohorts from North America. Factors with moderate or higher certainty of association with increased risk of VTE include older age, obesity, male sex, higher Injury Severity Score, pelvic injury, lower extremity injury, spinal injury, delayed VTE prophylaxis, need for surgery, and tranexamic acid use. After accounting for other important contributing prognostic variables, a delay in the delivery of appropriate pharmacologic prophylaxis for as little as 24 to 48 hours independently confers a clinically meaningful twofold increase in incidence of VTE.

CONCLUSION

These findings highlight the contribution of patient predisposition, the importance of injury pattern, and the impact of potentially modifiable postinjury care on risk of VTE after traumatic injury. These factors should be incorporated into a risk stratification framework to individualize VTE risk assessment and support clinical and academic efforts to reduce thromboembolic events among trauma patients.

LEVEL OF EVIDENCE

Systematic Review and Meta-Analysis; Level III.

Incremental value of serum neurofilament light chain and glial fibrillary acidic protein as blood-based biomarkers for predicting functional outcome in severe acute ischemic stroke

INTRODUCTION

Blood-based biomarkers may improve prediction of functional outcome in patients with acute ischemic stroke. The role of neurofilament light chain (NfL) and glial fibrillary acidic (GFAP) as potential biomarkers especially in severe stroke patients is unknown.

PATIENTS AND METHODS

Prospective, monocenter, cohort study including consecutive patients with severe ischemic stroke in the anterior circulation on admission (NIHSS score ⩾ 6 points or indication for mechanical thrombectomy). Outcome was assessed 3 months after the index stroke by the modified Rankin Scale (mRS). Serum biomarkers levels of NfL and GFAP were determined by ultrasensitive ELISA. Univariate and multivariate logistic regression models were performed to determine the association of biomarker levels and functional disability. Discrimination, calibration, and overall performance were analyzed in different models via AUROC, calibration plots (with Emax and Eavg), Brier-score and R2 using variables, identified as important covariates for functional outcome in previous studies.

RESULTS

Between 06/2020 and 08/2021, 213 patients were included [47% female, mean age 76 (SD ± 12) years, median NIHSS score 13 (interquartile range, IQR 9; 17)]. Biomarker serum levels were measured at a median of 1 [IQR, 1; 2] day after admission. Compared to patients with mRS 0-2 at 3 months, patients with mRS 3-6 had higher serum levels of NfL (median: 136 pg/ml vs 41 pg/ml; p < 0.0001) and GFAP (700 ng/ml vs 9.6 ng/ml; p < 0.0001). Both biomarkers were significantly associated with functional outcome [adjusted logistic regression, odds ratio (95% CI) for NfL: 2.63 (1.62; 4.56), GFAP: 2.16 (1.58; 3.09)]. In all models the addition of serum NfL led to a significant improvement in the AUROC, as did the addition of serum GFAP. Calibration plots showed high agreement between the predicted and observed outcomes and after addition of the two blood-based biomarkers there was an improvement of the overall performance.

CONCLUSION

Prediction of functional outcome after severe acute ischemic stroke was improved by the blood-based biomarkers serum NfL and GFAP, measured in the acute phase of stroke. These findings have to be replicated in independent external cohorts.Study registration: DRKS00022064.

Prediction of adolescent idiopathic scoliosis with machine learning algorithms using brain volumetric measurements

Background

It is known that neuroanatomical and neurofunctional changes observed in the brain, brainstem and cerebellum play a role in the etiology of adolescent idiopathic scoliosis (AIS). This study aimed to investigate whether volumetric measurements of brain regions can be used as predictive indicators for AIS through machine learning techniques.

Methods

Patients with a severe degree of curvature in AIS (n = 32) and healthy individuals (n = 31) were enrolled in the study. Volumetric data from 169 brain regions, acquired from magnetic resonance imaging (MRI) of these individuals, were utilized as predictive factors. A comprehensive analysis was conducted using the twelve most prevalent machine learning algorithms, encompassing thorough parameter adjustments and cross-validation processes. Furthermore, the findings related to variable significance are presented.

Results

Among all the algorithms evaluated, the random forest algorithm produced the most favorable results in terms of various classification metrics, including accuracy (0.9083), AUC (0.993), f1-score (0.970), and Brier score (0.1256). Additionally, the most critical variables were identified as the volumetric measurements of the right corticospinal tract, right corpus callosum body, right corpus callosum splenium, right cerebellum, and right pons, respectively.

Conclusion

The outcomes of this study indicate that volumetric measurements of specific brain regions can serve as reliable indicators of AIS. In conclusion, the developed model and the significant variables discovered hold promise for predicting scoliosis development, particularly in high-risk individuals.

Planned but ever published? A retrospective analysis of clinical prediction model studies registered on clinicaltrials.gov since 2000

OBJECTIVES

To describe the characteristics and publication outcomes of clinical prediction model studies registered on clinicaltrials.gov since 2000.

STUDY DESIGN AND SETTING

Observational studies registered on clinicaltrials.gov between January 1, 2000, and March 2, 2022, describing the development of a new clinical prediction model or the validation of an existing model for predicting individual-level prognostic or diagnostic risk were analyzed. Eligible clinicaltrials.gov records were classified by modeling study type (development, validation) and the model outcome being predicted (prognostic, diagnostic). Recorded characteristics included study status, sample size information, Medical Subject Headings, and plans to share individual participant data. Publication outcomes were analyzed by linking National Clinical Trial numbers for eligible records with PubMed abstracts.

RESULTS

Nine hundred twenty-eight records were analyzed from a possible 89,896 observational study records. Publications searches found 170 matching peer-reviewed publications for 137 clinicaltrials.gov records. The estimated proportion of records with 1 or more matching publications after accounting for time since study start was 2.8% at 2 years (95% CI: 1.7%, 3.9%), 12.3% at 5 years (9.8% to 14.9%) and 27% at 10 years (23% to 33%). Stratifying records by study start year indicated that publication proportions improved over time. Records tended to prioritize the development of new prediction models over the validation of existing models (76%; 704/928 vs. 24%; 182/928). At the time of download, 27% of records were marked as complete, 35% were still recruiting, and 14.7% had unknown status. Only 7.4% of records stated plans to share individual participant data.

CONCLUSION

Published clinical prediction model studies are only a fraction of overall research efforts, with many studies planned but not completed or published. Improving the uptake of study preregistration and follow-up will increase the visibility of planned research. Introducing additional registry features and guidance may improve the identification of clinical prediction model studies posted to clinical registries.

Risk factors and predictors of venous thromboembolism in patients with acute spontaneous intracerebral hemorrhage: A systematic review and meta-analysis

BACKGROUND

Venous thromboembolism (VTE), including deep vein thrombosis (DVT) and pulmonary embolism (PE), is a common and preventable complication of patients with acute spontaneous intracerebral hemorrhages (ICH). Knowledge of VTE risk factors in patients with acute spontaneous ICH continues to evolve while remains controversial. Therefore, this study aims to summarize the risk factors and predictors of VTE in patients with acute spontaneous ICH.

METHODS

EMBASE, PubMed, Web of Science and Cochrane databases were searched for articles containing Mesh words "Cerebral hemorrhage" and "Venous thromboembolism." Eligibility screening, data extraction, and quality assessment of the retrieved articles were conducted independently by two reviewers. We performed meta-analysis to determine risk factors for the development of VTE in acute spontaneous ICH patients. Sensitivity analysis were performed to explore the sources of heterogeneity.

RESULTS

Of the 12,362 articles retrieved, 17 cohort studies were included.Meta-analysis showed that longer hospital stay [OR=15.46, 95 % CI (12.54, 18.39), P<0.00001], infection [OR=5.59, 95 % CI (1.53, 20.42), P=0.009], intubation [OR=4.32, 95 % CI (2.79, 6.69), P<0.00001] and presence of intraventricular hemorrhage (IVH) [OR=1.89, 95 % CI (1.50, 2.38), P<0.00001] were significant risk factors for VTE in acute spontaneous ICH patients. Of the 17 studies included, five studies reported six prediction models, including 15 predictors. The area under the receiver operating curve (AUC) ranged from 0.71 to 0.95. One of the models was externally validated.

CONCLUSION

Infection, the intubation, presence of IVH and longer hospital stay were risk factors for the development of VTE in acute spontaneous ICH patients. Prediction models of VTE based on acute spontaneous ICH patients have been poorly reported and more research will be needed before such models can be applied in clinical settings.

Development of a Prediction Model for Surgery or Early Mortality at the Time of Initial Assessment for Necrotizing Enterocolitis

OBJECTIVE

 No available scale, at the time of initial evaluation for necrotizing enterocolitis (NEC), accurately predicts, that is, with an area under the curve (AUC) ≥0.9, which preterm infants will undergo surgery for NEC stage III or die within a week.

STUDY DESIGN

 This is a retrospective cohort study (n = 261) of preterm infants with <33 weeks' gestation or <1,500 g birth weight with either suspected or with definite NEC born at Parkland Hospital between 2009 and 2021. A prediction model using the new HASOFA score (Hyperglycemia, Hyperkalemia, use of inotropes for Hypotension during the prior week, Acidemia, Neonatal Sequential Organ Failure Assessment [nSOFA] score) was compared with a similar model using the nSOFA score.

RESULTS

 Among 261 infants, 112 infants had NEC stage I, 68 with NEC stage II, and 81 with NEC stage III based on modified Bell's classification. The primary outcome, surgery for NEC stage III or death within a week, occurred in 81 infants (surgery in 66 infants and death in 38 infants). All infants with pneumoperitoneum or abdominal compartment syndrome either died or had surgery. The HASOFA and the nSOFA scores were evaluated in 254 and 253 infants, respectively, at the time of the initial workup for NEC. Both models were internally validated. The HASOFA model was a better predictor of surgery for NEC stage III or death within a week than the nSOFA model, with greater AUC 0.909 versus 0.825, respectively, p < 0.001. Combining HASOFA at initial assessment with concurrent or later presence of abdominal wall erythema or portal gas improved the prediction surgery for NEC stage III or death with AUC 0.942 or 0.956, respectively.

CONCLUSION

 Using this new internally validated prediction model, surgery for NEC stage III or death within a week can be accurately predicted at the time of initial assessment for NEC.

KEY POINTS

· No available scale, at initial evaluation, accurately predicts which preterm infants will undergo surgery for NEC stage III or die within a week.. · In this retrospective cohort study of 261 preterm infants with either suspected or definite NEC we developed a new prediction model (HASOFA score).. · The HASOFA-model had high discrimination (AUC: 0.909) and excellent calibration and was internally validated..

Preoperative risk prediction models for acute kidney injury after noncardiac surgery: an independent external validation cohort study

BACKGROUND

Numerous models have been developed to predict acute kidney injury (AKI) after noncardiac surgery, yet there is a lack of independent validation and comparison among them.

METHODS

We conducted a systematic literature search to review published risk prediction models for AKI after noncardiac surgery. An independent external validation was performed using a retrospective surgical cohort at a large Chinese hospital from January 2019 to October 2022. The cohort included patients undergoing a wide range of noncardiac surgeries with perioperative creatinine measurements. Postoperative AKI was defined according to the Kidney Disease Improving Global Outcomes creatinine criteria. Model performance was assessed in terms of discrimination (area under the receiver operating characteristic curve, AUROC), calibration (calibration plot), and clinical utility (net benefit), before and after model recalibration through intercept and slope updates. A sensitivity analysis was conducted by including patients without postoperative creatinine measurements in the validation cohort and categorising them as non-AKI cases.

RESULTS

Nine prediction models were evaluated, each with varying clinical and methodological characteristics, including the types of surgical cohorts used for model development, AKI definitions, and predictors. In the validation cohort involving 13,186 patients, 650 (4.9%) developed AKI. Three models demonstrated fair discrimination (AUROC between 0.71 and 0.75); other models had poor or failed discrimination. All models exhibited some miscalibration; five of the nine models were well-calibrated after intercept and slope updates. Decision curve analysis indicated that the three models with fair discrimination consistently provided a positive net benefit after recalibration. The results were confirmed in the sensitivity analysis.

CONCLUSIONS

We identified three models with fair discrimination and potential clinical utility after recalibration for assessing the risk of acute kidney injury after noncardiac surgery.

An international study to investigate and optimise the safety of discontinuing valproate in young men and women with epilepsy: Protocol

Valproate is the most effective treatment for idiopathic generalised epilepsy. Currently, its use is restricted in women of childbearing potential owing to high teratogenicity. Recent evidence extended this risk to men's offspring, prompting recommendations to restrict use in everybody aged <55 years. This study will evaluate mortality and morbidity risks associated with valproate withdrawal by emulating a hypothetical randomised-controlled trial (called a "target trial") using retrospective observational data. The data will be drawn from ~250m mainly US patients in the TriNetX repository and ~60m UK patients in Clinical Practice Research Datalink (CPRD). These will be scanned for individuals aged 16-54 years with epilepsy and on valproate who either continued, switched to lamotrigine or levetiracetam, or discontinued valproate between 2014-2024, creating four groups. Randomisation to these groups will be emulated by baseline confounder adjustment using g-methods. Mortality and morbidity outcomes will be assessed and compared between groups over 1-10 years, employing time-to-first-event and recurrent events analyses. A causal prediction model will be developed from these data to aid in predicting the safest alternative antiseizure medications. Together, these findings will optimise informed decision-making about valproate withdrawal and alternative treatment selection, providing immediate and vital information for patients, clinicians and regulators.

Identification of variables and development of a prediction model for DIBH eligibility in left-sided breast cancer radiotherapy: a prospective cohort study with temporal validation

OBJECTIVE

To identify variables associated with a patients' ability to reproducibly hold their breath for deep-inspiration breath-hold (DIBH) radiotherapy (RT) and to develop a predictive model for DIBH eligibility.

METHODS

This prospective, single-institution, IRB-approved observational study included women with left-sided breast cancer treated between January 2023 and March 2024. Patients underwent multiple breath-hold sessions over 2-3 consecutive days. DIBH waveform metrics and clinical factors were recorded and analysed. Logistic mixed modelling was used to predict DIBH eligibility, and a temporal validation cohort was used to assess model performance.

RESULTS

In total, 253 patients were included, with 206 in the model development cohort and 47 in the temporal validation cohort. The final logistic mixed model identified increasing average breath-hold duration (OR, 95% CI: 0.308, 0.104-0.910. p = 0.033) and lower amplitude (OR, 95% CI: 0.737, 0.641-0.848. p < 0.001) as significant predictors of DIBH eligibility. Increasing age was associated with higher odds of being ineligible for DIBH (OR, 95% CI: 1.040, 1.001-1.081. p = 0.044). The model demonstrated good discriminative performance in the validation cohort with an AUC of 80.9% (95% CI: 73.0-88.8).

CONCLUSION

The identification of variables associated with DIBH eligibility and development of a predictive model has the potential to serve as a decision-support tool. Further external validation is required before its integration into routine clinical practice.

Development and internal validation of a multivariable prognostic model to predict chronic pain after a new episode of non-specific idiopathic, non-traumatic neck pain in physiotherapy primary care practice

OBJECTIVE

To develop and internally validate a prognostic model to predict chronic pain after a new episode of acute or subacute non-specific idiopathic, non-traumatic neck pain in patients presenting to physiotherapy primary care, emphasising modifiable biomedical, psychological and social factors.

DESIGN

A prospective cohort study with a 6-month follow-up between January 2020 and March 2023.

SETTING

30 physiotherapy primary care practices.

PARTICIPANTS

Patients with a new presentation of non-specific idiopathic, non-traumatic neck pain, with a duration lasting no longer than 12 weeks from onset.

BASELINE MEASURES

Candidate prognostic variables collected from participants included age and sex, neck pain symptoms, work-related factors, general factors, psychological and behavioural factors and the remaining factors: therapeutic relation and healthcare provider attitude.

OUTCOME MEASURES

Pain intensity at 6 weeks, 3 months and 6 months on a Numeric Pain Rating Scale (NPRS) after inclusion. An NPRS score of ≥3 at each time point was used to define chronic neck pain.

RESULTS

62 (10%) of the 603 participants developed chronic neck pain. The prognostic factors in the final model were sex, pain intensity, reported pain in different body regions, headache since and before the neck pain, posture during work, employment status, illness beliefs about pain identity and recovery, treatment beliefs, distress and self-efficacy. The model demonstrated an optimism-corrected area under the curve of 0.83 and a corrected R2 of 0.24. Calibration was deemed acceptable to good, as indicated by the calibration curve. The Hosmer-Lemeshow test yielded a p-value of 0.7167, indicating a good model fit.

CONCLUSION

This model has the potential to obtain a valid prognosis for developing chronic pain after a new episode of acute and subacute non-specific idiopathic, non-traumatic neck pain. It includes mostly potentially modifiable factors for physiotherapy practice. External validation of this model is recommended.

Prognostic prediction models for treatment experienced people living with HIV: a protocol for systematic review and meta-analysis

INTRODUCTION

Despite the favourable efficacy of antiretroviral therapy (ART), HIV/AIDS continues to impose significant disease burdens worldwide. This study aims to systematically review published prognostic prediction models for survival outcomes of treatment experienced people living with HIV (TE-PLHIV), to describe their characteristics, compare their performance and assess the risk of bias and real-world clinical utility.

METHODS AND ANALYSIS

Studies will be identified through a comprehensive search in PubMed, EMBASE, Scopus, the Cochrane Library, and OpenGrey databases. Two reviewers will independently conduct a selection of eligible studies, data extraction and critical appraisal. Included studies will be systematically summarised using appropriate tools designed for prognostic prediction modelling studies. Where applicable, evidence will be summarised with meta-analyses.

ETHICS AND DISSEMINATION

Ethical approval is not required because only available published data will be analysed. The results of this work will be published in a peer-reviewed journal.

SYSTEMATIC REVIEW REGISTRATION

PROSPERO registration number CRD42023412118.

Development and application of a serious adverse events risk model for concurrent chemoradiotherapy in patients with nasopharyngeal carcinoma

The objective of this study was to construct a concise prediction model for serious adverse events (SAEs) in order to assess the likelihood of SAE occurrence among hospitalized patients undergoing concurrent chemoradiotherapy. An electronic database of a Cancer Centre was utilized to conduct a cross-sectional review survey. Our research involved the recruitment of 239 patients who were undergoing concurrent chemoradiotherapy in the Department of Nasopharynx and Radiotherapy. The clinical prediction rule was derived using logistic regression analysis, with SAE serving as the primary outcome. Internal verification was conducted. The occurrence rate of SAE in the derivation cohort was 59.4%. The ultimate model used had 3 variables, namely cystatin C, C-reactive protein, and serum amyloid A. The model exhibited an area under the curve of 0.626 (95% CI: 0.555-0.696; P < .001). The model accurately predicts the occurrence of SAE, and the variable data can be easily obtained, and the assessment technique is straightforward.

Identifying phenotypes in persons with temporomandibular disorders, using latent class analyses: Temporomandibular disorders and phenotypes

BACKGROUND

The heterogeneity of persons with temporomandibular disorders (TMD) and the lack of effective treatments have called for a biopsychosocial model and the development of a more personalised treatment approach. Emphasis on phenotypes might be a beneficial approach.

OBJECTIVE

Identifying phenotypes among persons with TMD using potential prognostic factors, including personal characteristics and responses to clinical tests. Additionally, examining the distribution of TMD diagnoses within the identified phenotypes.

METHODS

A cross-sectional study including 208 persons (85% females) seeking physiotherapy for problems in the temporomandibular area. All participants were examined clinically and answered questionnaires electronically. The phenotypes were identified using latent class analysis based on seven potential prognostic factors selected within pain, function and psychological domains. Table analysis was used to explore the distribution of TMD diagnoses within the identified phenotypes.

RESULTS

Most participants fit into one of three identified phenotypes. Phenotype 1 (32%) was characterised by functional disability, low psychosocial scores and low risk for developing chronicity and future work disability; Phenotype 2 (29%) by parafunctional habits, low psychosocial score and seeking treatment to reduce pain; and Phenotype 3 (39%) by high levels of mental distress, fear avoidance and a large risk of future work disability. Intra-articular disorders dominated Phenotype 1, myalgia and TMD-related headache Phenotype 2, while Phenotype 3 included all the different TMD diagnoses.

CONCLUSION

The knowledge about the three identified phenotypes might be useful for clinicians treating persons with TMD and for the development of preventive strategies and more personalised treatment.

Prevalence of undetected chronic kidney disease in high-risk middle-aged patients in primary care: a cross-sectional study

Introduction

The global health burden of chronic kidney disease (CKD) results from both the disease itself and the numerous health problems associated with it. The aim of this study was to estimate the prevalence of previously undetected CKD in middle-aged patients with risk factors for CKD. Identified patients were included in the Styrian nephrology awareness program "kidney.care 2.0" and data on their demographics, risk factors and kidney function were described.

Methods

Cross-sectional analysis of baseline data derived from the "kidney.care 2.0" study of 40-65 year old patients with at least one risk factor for CKD (hypertension, diabetes, cardiovascular disease, obesity or family history of end-stage kidney disease). Participants were considered to have previously undetected CKD if their estimated glomular filtration rate (eGFR) was less than 60 ml/min/1.73 m2 and/or albumin creatinine ratio (ACR) ≥ 30 mg/g. We calculated the prevalence of previously undetected CKD and performed multivariate analyses.

Results

A total of 749 participants were included in this analysis. The prevalence of previously undetected CKD in an at-risk population was estimated at 20.1% (95%CI: 17.1-23.6). Multivariable analysis showed age (OR 1.06, 95%CI: 1.02-1.09), diabetes mellitus (OR 1.65, 95%CI: 1.12-2.30) and obesity (OR: 1.55, 95%CI: 1.04-2.30) to be independent predictors of CKD. The majority of patients with previously undetected CKD had category A2-A3 albuminuria (121 out of 150). Most patients with previously undetected eGFR < 60 ml/min/1.73 m2 were in stage G3 (36 out of 39 patients).

Discussion

Pragmatic, targeted, risk-based screening for CKD in primary care successfully identified a significant number of middle-aged patients with previously undetected CKD and addressed the problem of these patients being overlooked for future optimized care. The intervention may slow progression to kidney failure and prevent related cardiovascular events.

Predictive factors and dose-response effect of rehabilitation for upper limb induced recovery after stroke: systematic review with proportional meta-analyses

BACKGROUND AND PURPOSE

To date, factors with predictive value for upper limb (UL) recovery after stroke are acknowledged, but little is known on clinical features predicting outcome in response to rehabilitation. The purpose of this review is to investigate whether any factor allows identification of Responders to rehabilitation, and whether clinically important recovery of motor function relies on modalities and dose of intervention received, at different times after stroke.

METHODS

A systematic review with proportional meta-analysis was conducted. Longitudinal single-cohort studies on patients undergoing rehabilitation after stroke were included. Predictive features investigated in the included studies were reported. The primary outcome was the Fugl-Meyer Assessment for Upper Extremity, and effect sizes (ES) of different rehabilitation doses were calculated.

RESULTS

Only 6% of the included studies (n = 141) investigated predictive factors. Studies providing more than 30 hours of therapy induced small to large clinical effect (ES from 0.38 to 0.88). Task-oriented approach led to the largest effect, both in the subacute (ES = 0.88) and chronic (ES = 0.71) phases. Augmenting interventions provided higher effect in the chronic rather than subacute phase. Integrity of the corticospinal tract, preservation of arm motor function and specific genetic biomarkers were found to be associated with motor recovery DISCUSSION AND CONCLUSIONS: Trials on motor recovery after stroke should incorporate analysis of factors associated with rehabilitation outcomes. Task-oriented interventions should be delivered more than 30 hours (high dose) to induce the greatest improvement.

SYSTEMATIC REVIEW REGISTRATION NUMBER

Systematic Review Registration Number PROSPERO CRD42021258188. CONTRIBUTION OF THE PAPER.

The adverse inpatient medication event and frailty (AIME-frail) risk prediction model

BACKGROUND

Medication harm affects between 5 and 15% of hospitalised patients, with approximately half of the harm events considered preventable through timely intervention. The Adverse Inpatient Medication Event (AIME) risk prediction model was previously developed to guide a systematic approach to patient prioritisation for targeted clinician review, but frailty was not tested as a candidate predictor variable.

AIM

To evaluate the predictive performance of an updated AIME model, incorporating a measure of frailty, when applied to a new multisite cohort of hospitalised adult inpatients.

METHODS

A retrospective cohort study was conducted at two tertiary Australian hospitals on patients discharged between 1st January and April 31, 2020. Data were extracted from electronic medical records (EMRs) and clinical coding databases. Medication harm was identified using ICD-10 Y-codes and confirmed by senior pharmacist review of medical records. The Hospital Frailty Risk Score (HFRS) was calculated for each patient. Logistic regression analysis was used to construct a modified AIME model. Candidate variables of the original AIME model, together with new variables including HFRS were tested. Performance of the final model was reported using area under the curve (AUC) and decision curve analysis (DCA).

RESULTS

A total of 4089 patient admissions were included, with a mean age ± standard deviation (SD) of 64 years (±19 years), 2050 patients (50%) were males, and mean HFRS was 6.2 (±5.9). 184 patients (4.5%) experienced one or more medication harm events during hospitalisation. The new AIME-Frail risk model incorporated 5 of the original variables: length of stay (LOS), anti-psychotics, antiarrhythmics, immunosuppressants, and INR greater than 3, as well as 5 new variables: HFRS, anticoagulants, antibiotics, insulin, and opioid use. The AUC was 0.79 (95% CI: 0.76-0.83) which was superior to the original model (AUC = 0.70, 95% CI: 0.65-0.74) with a sensitivity of 69%, specificity of 81%, positive predictive value of 0.14 (95% CI: 0.10-0.17) and negative predictive value of 0.98 (95% CI: 0.97-0.99). The DCA identified the model as having potential clinical utility between the probability thresholds of 0.05-0.4.

CONCLUSION

The inclusion of a frailty measure improved the predictive performance of the AIME model. Screening inpatients using the AIME-Frail tool could identify more patients at high-risk of medication harm who warrant timely clinician review.

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis

BACKGROUND

 The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with conventional methods often limited by subjectivity and variability in interpretation. Integrating deep learning with artificial intelligence (AI) in magnetic resonance imaging (MRI) analysis emerges as a transformative approach, offering the potential for unbiased, highly accurate diagnostic insights.

OBJECTIVE

 A meta-analysis was designed to analyze the diagnostic accuracy of deep learning of MRI images on AD and MCI models.

METHODS

 A meta-analysis was performed across PubMed, Embase, and Cochrane library databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, focusing on the diagnostic accuracy of deep learning. Subsequently, methodological quality was assessed using the QUADAS-2 checklist. Diagnostic measures, including sensitivity, specificity, likelihood ratios, diagnostic odds ratio, and area under the receiver operating characteristic curve (AUROC) were analyzed, alongside subgroup analyses for T1-weighted and non-T1-weighted MRI.

RESULTS

 A total of 18 eligible studies were identified. The Spearman correlation coefficient was -0.6506. Meta-analysis showed that the combined sensitivity and specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were 0.84, 0.86, 6.0, 0.19, and 32, respectively. The AUROC was 0.92. The quiescent point of hierarchical summary of receiver operating characteristic (HSROC) was 3.463. Notably, the images of 12 studies were acquired by T1-weighted MRI alone, and those of the other 6 were gathered by non-T1-weighted MRI alone.

CONCLUSION

 Overall, deep learning of MRI for the diagnosis of AD and MCI showed good sensitivity and specificity and contributed to improving diagnostic accuracy.

How to develop, validate, and update clinical prediction models using multinomial logistic regression

OBJECTIVES

Multicategory prediction models (MPMs) can be used in health care when the primary outcome of interest has more than two categories. The application of MPMs is scarce, possibly due to added methodological complexities compared to binary outcome models. We provide a guide of how to develop, validate, and update clinical prediction models based on multinomial logistic regression.

STUDY DESIGN AND SETTING

We present guidance and recommendations based on recent methodological literature, illustrated by a previously developed and validated MPM for treatment outcomes in rheumatoid arthritis. Prediction models using multinomial logistic regression can be developed for nominal outcomes, but also for ordinal outcomes. This article is intended to supplement existing general guidance on prediction model research.

RESULTS

This guide is split into three parts: 1) outcome definition and variable selection, 2) model development, and 3) model evaluation (including performance assessment, internal and external validation, and model recalibration). We outline how to evaluate and interpret the predictive performance of MPMs. R code is provided.

CONCLUSION

We recommend the application of MPMs in clinical settings where the prediction of a multicategory outcome is of interest. Future methodological research could focus on MPM-specific considerations for variable selection and sample size criteria for external validation.

Validation of a model for outcome prediction after endovascular treatment for ischemic stroke

INTRODUCTION

The recently developed MR-PREDICTS@24 h model showed excellent performance in the MR-CLEAN Registry cohort in patients presenting within 12 h from onset. However, its applicability to an U.S. population and to patients presenting beyond 12 h from last known normal are still undetermined. We aim to externally validate the MR-PREDICTS@24 h model in a new geographic setting and in the late window.

METHODS

In this retrospective analysis of a prospectively collected database from a comprehensive stroke center in the United States, we included patients with intracranial carotid artery or middle cerebral artery M1 or M2 segment occlusions who underwent endovascular therapy and applied the MR-PREDICTS@24 h formula to estimate the probabilities of functional outcome at day 90. The primary endpoint was the modified Rankin Scale (mRS) at 90 days.

RESULTS

We included 1246 patients, 879 in the early (<12 h) and 367 in the late (≥12 h) cohort. For both cohorts, calibration and discrimination of the model were accurate throughout mRS levels, with absolute differences between estimated and predicted proportions ranging from 1% to 5%. Calibration metrics and curve inspections showed good performance for estimating the probabilities of mRS ≤ 1 to mRS ≤ 5 for the early cohort. For the late cohort, predictions were reliable for the probabilities of mRS ≤ 1 to mRS ≤ 4.

CONCLUSION

The MR-PREDICTS@24 h was transferrable to a real-world U.S.-based cohort in the early window and showed consistently accurate predictions for patients presenting in the late window without need for updating.

A novel clinical prediction model for in-hospital mortality in sepsis patients complicated by ARDS: A MIMIC IV database and external validation study

Background

Sepsis complicated by ARDS significantly increases morbidity and mortality, underscoring the need for robust predictive models to enhance patient management.

Methods

We collected data on 6390 patients with ARDS-complicated sepsis from the MIMIC IV database. Following rigorous data cleaning, including outlier management, handling missing values, and transforming variables, we conducted univariate analysis and logistic multivariate regression. We employed the LASSO machine learning algorithm to identify risk factors closely associated with patient outcomes. These factors were then used to develop a new clinical prediction model. The model underwent preliminary assessment and internal validation, and its performance was further tested through external validation using data from 225 patients at a major tertiary hospital in China. This validation assessed the model's discrimination, calibration, and net clinical benefits.

Results

The model, illustrated by a concise nomogram, demonstrated significant discrimination with an area under the curve (AUC) of 0.711 in the internal validation set and 0.771 in the external validation set, outperforming conventional severity scores such as the SOFA and SAPS II. It also showed good calibration and net clinical benefits.

Conclusions

Our model serves as a valuable tool for identifying sepsis patients with ARDS at high risk of in-hospital mortality. This could enable the implementation of personalized treatment strategies, potentially improving patient outcomes.

Outcomes of prognostication in people living with advanced cancer: A qualitative study to inform a Core Outcome Set

BACKGROUND

Studies of prognostication in advanced cancer use a wide range of outcomes and outcome measures, making it difficult to compare these studies and their findings. Core Outcome Sets facilitate comparability and standardisation between studies and would benefit future prognostic research. This qualitative study, the second step in a wider study developing such a Core Outcome Set, aimed to explore the perceptions and experiences of patients with advanced cancer, informal caregivers, and clinicians regarding the potential outcomes to assess the impact of prognostication.

METHODS

We conducted semi-structured interviews with patients living with advanced cancer (n = 8), informal caregivers (n = 10), and clinicians (n = 10) recruited from palliative care services across three sites in London, United Kingdom. Interviews were conducted in-person, via telephone, or video conferencing, and were audio-recorded. Data were analysed using framework analysis. Findings were compared with outcomes identified in a previously published systematic review.

RESULTS

We identified 33 outcomes, 16 of which were not previously reported in the literature. We grouped these outcomes into 10 domains, nine from the COMET taxonomy, plus a tenth domain (spiritual/religious/existential functioning/wellbeing) which we added further to the previous systematic review. These findings highlighted discrepancies between the priorities of existing research and those of stakeholders. Novel outcomes highlight the more personal and emotional impacts of prognostication, whilst other outcomes confirm the relevance of survival length, depression, anxiety, pain, hope dynamics, emotional distress, and the quality of patient-clinician relationships for assessing the impact of prognostication.

CONCLUSIONS

This study offers valuable insights into outcomes which matter to key stakeholders, particularly patients and informal caregivers, highlights discrepancies between their priorities and those identified in previous studies, and underscores the need for a patient-centred approach in research and clinical practice in prognostication in advanced cancer. This work will contribute to developing a Core Outcome Set for assessing the impact of prognostication in advanced cancer.

Multivariate Analysis of Factors Associated with Feeding Mother's Own Milk at Discharge in Preterm Infants: A Retrospective Cohort Study

OBJECTIVE

 This study aimed to develop a predictive model of feeding mother's own milk (MOM) at discharge using social determinants of health (SDOH), maternal and neonatal factors after deliveries at <33 weeks of gestational age (GA), or birth weight <1,500 g.

STUDY DESIGN

 Secondary analysis of a retrospective cohort in an inner-city hospital before (Epoch-1, 2018-2019) and after (Epoch-2, 2020-2021) implementing a donor human milk (DHM) program.

RESULTS

 Among 986 neonates, 495 were born in Epoch-1 (320 Hispanic White, 142 Non-Hispanic Black, and 33 Other) and 491 in Epoch-2 (327, 137, and 27, respectively). Feeding any MOM was less frequent in infants of non-Hispanic Black mothers than in those of Hispanic mothers (p < 0.05) but did not change with epoch (p = 0.46). Among infants who received any MOM, continued feeding MOM to the time of discharge was less frequent in infants of non-Hispanic Black mothers versus those of Hispanic mothers, 94/237 (40%) versus 339/595 (57%; p < 0.05), respectively. In multivariate analysis including SDOH and maternal variables, the odds of feeding MOM at discharge were lower with SDOH including neighborhoods with higher poverty levels, multiparity, substance use disorder, non-Hispanic Black versus Hispanic and young maternal age and increased with GA but did not change after implementing DHM. The predictive model including SDOH, maternal and early neonatal variables had good discrimination (area under the curve 0.85) and calibration and was internally validated. It showed the odds of feeding MOM at discharge were lower in infants of non-Hispanic Black mothers and with feeding DHM, higher need for respiratory support and later initiation of feeding MOM.

CONCLUSION

 Feeding MOM at discharge was associated with SDOH, and maternal and neonatal factors but did not change after implementing DHM. Disparity in feeding MOM at discharge was explained by less frequent initiation and shorter duration of feeding MOM but not by later initiation of feeding MOM.

KEY POINTS

· In this cohort study of preterm infants, factors of feeding MOM at discharge included (1) SDOH; (2) postnatal age at initiation of feeding MOM; and (3) maternal and neonatal factors.. · Feeding MOM at the time of discharge was less frequent in infants of non-Hispanic Black mothers versus those of Hispanic mothers.. · Disparity in feeding MOM at discharge was explained by less frequent initiation and shorter duration of MOM feeding but not by later postnatal age at initiation of feeding MOM..

Ventilatory efficiency as a prognostic factor for postoperative complications in patients undergoing elective major surgery: a systematic review

BACKGROUND

Major surgery is associated with high complication rates. Several risk scores exist to assess individual patient risk before surgery but have limited precision. Novel prognostic factors can be included as additional building blocks in existing prediction models. A candidate prognostic factor, measured by cardiopulmonary exercise testing, is ventilatory efficiency (VE/VCO2). The aim of this systematic review was to summarise evidence regarding VE/VCO2 as a prognostic factor for postoperative complications in patients undergoing major surgery.

METHODS

A medical library specialist developed the search strategy. No database-provided limits, considering study types, languages, publication years, or any other formal criteria were applied to any of the sources. Two reviewers assessed eligibility of each record and rated risk of bias in included studies.

RESULTS

From 10,082 screened records, 65 studies were identified as eligible. We extracted adjusted associations from 32 studies and unadjusted from 33 studies. Risk of bias was a concern in the domains 'study confounding' and 'statistical analysis'. VE/VCO2 was reported as a prognostic factor for short-term complications after thoracic and abdominal surgery. VE/VCO2 was also reported as a prognostic factor for mid- to long-term mortality. Data-driven covariable selection was applied in 31 studies. Eighteen studies excluded VE/VCO2 from the final multivariable regression owing to data-driven model-building approaches.

CONCLUSIONS

This systematic review identifies VE/VCO2 as a predictor for short-term complications after thoracic and abdominal surgery. However, the available data do not allow conclusions about clinical decision-making. Future studies should select covariables for adjustment a priori based on external knowledge.

SYSTEMATIC REVIEW PROTOCOL

PROSPERO (CRD42022369944).

Machine Learning Algorithms Versus Classical Regression Models in Pre-Eclampsia Prediction: A Systematic Review

PURPOSE OF REVIEW

Machine learning (ML) approaches are an emerging alternative for healthcare risk prediction. We aimed to synthesise the literature on ML and classical regression studies exploring potential prognostic factors and to compare prediction performance for pre-eclampsia.

RECENT FINDINGS

From 9382 studies retrieved, 82 were included. Sixty-six publications exclusively reported eighty-four classical regression models to predict variable timing of onset of pre-eclampsia. Another six publications reported purely ML algorithms, whilst another 10 publications reported ML algorithms and classical regression models in the same sample with 8 of 10 findings that ML algorithms outperformed classical regression models. The most frequent prognostic factors were age, pre-pregnancy body mass index, chronic medical conditions, parity, prior history of pre-eclampsia, mean arterial pressure, uterine artery pulsatility index, placental growth factor, and pregnancy-associated plasma protein A. Top performing ML algorithms were random forest (area under the curve (AUC) = 0.94, 95% confidence interval (CI) 0.91-0.96) and extreme gradient boosting (AUC = 0.92, 95% CI 0.90-0.94). The competing risk model had similar performance (AUC = 0.92, 95% CI 0.91-0.92) compared with a neural network. Calibration performance was not reported in the majority of publications. ML algorithms had better performance compared to classical regression models in pre-eclampsia prediction. Random forest and boosting-type algorithms had the best prediction performance. Further research should focus on comparing ML algorithms to classical regression models using the same samples and evaluation metrics to gain insight into their performance. External validation of ML algorithms is warranted to gain insights into their generalisability.

MRI-based clinical-radiomics nomogram model for predicting microvascular invasion in hepatocellular carcinoma

BACKGROUND

Preoperative microvascular invasion (MVI) of liver cancer is an effective method to reduce the recurrence rate of liver cancer. Hepatectomy with extended resection and additional adjuvant or targeted therapy can significantly improve the survival rate of MVI+ patients by eradicating micrometastasis. Preoperative prediction of MVI status is of great clinical significance for surgical decision-making and the selection of other adjuvant therapy strategies to improve the prognosis of patients.

PURPOSE

Established a radiomics machine learning model based on multimodal MRI and clinical data, and analyzed the preoperative prediction value of this model for microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

METHOD

The preoperative liver MRI data and clinical information of 130 HCC patients who were pathologically confirmed to be pathologically confirmed were retrospectively studied. These patients were divided into MVI-positive group (MVI+) and MVI-negative group (MVI-) based on postoperative pathology. After a series of dimensionality reduction analysis, six radiomic features were finally selected. Then, linear support vector machine (linear SVM), support vector machine with rbf kernel function (rbf-SVM), logistic regression (LR), Random forest (RF) and XGBoost (XGB) algorithms were used to establish the MVI prediction model for preoperative HCC patients. Then, rbf-SVM with the best predictive performance was selected to construct the radiomics score (R-score). Finally, we combined R-score and clinical-pathology-image independent predictors to establish a combined nomogram model and corresponding individual models. The predictive performance of individual models and combined nomogram was evaluated and compared by receiver operating characteristic curve (ROC).

RESULT

Alpha-fetoprotein concentration, peritumor enhancement, maximum tumor diameter, smooth tumor margins, tumor growth pattern, presence of intratumor hemorrhage, and RVI were independent predictors of MVI. Compared with individual models, the final combined nomogram model (AUC: 0.968, 95% CI: 0.920-1.000) constructed by radiometry score (R-score) combined with clinicopathological parameters and apparent imaging features showed the optimal predictive performance.

CONCLUSION

This multi-parameter combined nomogram model had a good performance in predicting MVI of HCC, and had certain auxiliary value for the formulation of surgical plan and evaluation of prognosis.

Combining randomized and non-randomized data to predict heterogeneous effects of competing treatments

Some patients benefit from a treatment while others may do so less or do not benefit at all. We have previously developed a two-stage network meta-regression prediction model that synthesized randomized trials and evaluates how treatment effects vary across patient characteristics. In this article, we extended this model to combine different sources of types in different formats: aggregate data (AD) and individual participant data (IPD) from randomized and non-randomized evidence. In the first stage, a prognostic model is developed to predict the baseline risk of the outcome using a large cohort study. In the second stage, we recalibrated this prognostic model to improve our predictions for patients enrolled in randomized trials. In the third stage, we used the baseline risk as effect modifier in a network meta-regression model combining AD, IPD randomized clinical trial to estimate heterogeneous treatment effects. We illustrated the approach in the re-analysis of a network of studies comparing three drugs for relapsing-remitting multiple sclerosis. Several patient characteristics influence the baseline risk of relapse, which in turn modifies the effect of the drugs. The proposed model makes personalized predictions for health outcomes under several treatment options and encompasses all relevant randomized and non-randomized evidence.

Clinical prediction models for treatment outcomes in newly diagnosed epilepsy: A systematic review

Up to 35% of individuals diagnosed with epilepsy continue to have seizures despite treatment, commonly referred to as drug-resistant epilepsy. Uncontrolled seizures can directly, or indirectly, negatively impact an individual's quality of life. To inform clinical management and life decisions, it is important to be able to predict the likelihood of seizure control. Those likely to achieve seizure control will be able to return sooner to their usual work and leisure activities and require less follow-up, whereas those with a poor prognosis will need more frequent clinical attendance and earlier consideration of epilepsy surgery. This is a systematic review aimed at identifying demographic, clinical, physiological (e.g., electroencephalographic), and imaging (e.g., magnetic resonance imaging) factors that may be predictive of treatment outcomes in patients with newly diagnosed epilepsy (NDE). MEDLINE and Embase were searched for prediction models of treatment outcomes in patients with NDE. Study characteristics were extracted and subjected to assessment of risk of bias (and applicability concerns) using the PROBAST (Prediction Model Risk of Bias Assessment Tool) tool. Baseline variables associated with treatment outcomes are reported as prognostic factors. After screening, 48 models were identified in 32 studies, which generally scored low for concerns of applicability, but universally scored high for susceptibility to bias. Outcomes reported fit broadly into four categories: drug resistance, short-term treatment response, seizure remission, and mortality. Prognostic factors were also heterogenous, but the predictors that were commonly significantly associated with outcomes were those related to seizure characteristics/types, epilepsy history, and age at onset. Antiseizure medication response was often included as a baseline variable, potentially obscuring other factor relationships at baseline. Currently, outcome prediction models for NDE demonstrate a high risk of bias. Model development could be improved with a stronger adherence to recommended TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) practices. Furthermore, we outline actionable changes to common practices that are intended to improve the overall quality of prediction model development in NDE.

An Appraisal of the Quality of Development and Reporting of Predictive Models in Neurosurgery: A Systematic Review

BACKGROUND AND OBJECTIVES

Significant evidence has indicated that the reporting quality of novel predictive models is poor because of confounding by small data sets, inappropriate statistical analyses, and a lack of validation and reproducibility. The Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis (TRIPOD) statement was developed to increase the generalizability of predictive models. This study evaluated the quality of predictive models reported in neurosurgical literature through their compliance with the TRIPOD guidelines.

METHODS

Articles reporting prediction models published in the top 5 neurosurgery journals by SCImago Journal Rank-2 (Neurosurgery, Journal of Neurosurgery, Journal of Neurosurgery: Spine, Journal of NeuroInterventional Surgery, and Journal of Neurology, Neurosurgery, and Psychiatry) between January 1st, 2018, and January 1st, 2023, were identified through a PubMed search strategy that combined terms related to machine learning and prediction modeling. These original research articles were analyzed against the TRIPOD criteria.

RESULTS

A total of 110 articles were assessed with the TRIPOD checklist. The median compliance was 57.4% (IQR: 50.0%-66.7%). Models using machine learning-based models exhibited lower compliance on average compared with conventional learning-based models (57.1%, 50.0%-66.7% vs 68.1%, 50.2%-68.1%, P = .472). Among the TRIPOD criteria, the lowest compliance was observed in blinding the assessment of predictors and outcomes (n = 7, 12.7% and n = 10, 16.9%, respectively), including an informative title (n = 17, 15.6%) and reporting model performance measures such as confidence intervals (n = 27, 24.8%). Few studies provided sufficient information to allow for the external validation of results (n = 26, 25.7%).

CONCLUSION

Published predictive models in neurosurgery commonly fall short of meeting the established guidelines laid out by TRIPOD for optimal development, validation, and reporting. This lack of compliance may represent the minor extent to which these models have been subjected to external validation or adopted into routine clinical practice in neurosurgery.

Systematic review of multivariable prognostic models for outcomes at least 30 days after hip fracture finds 18 mortality models but no nonmortality models warranting validation

OBJECTIVES

Prognostic models have the potential to aid clinical decision-making after hip fracture. This systematic review aimed to identify, critically appraise, and summarize multivariable prediction models for mortality or other long-term recovery outcomes occurring at least 30 days after hip fracture.

STUDY DESIGN AND SETTING

MEDLINE, Embase, Scopus, Web of Science, and CINAHL databases were searched up to May 2023. Studies were included that aimed to develop multivariable models to make predictions for individuals at least 30 days after hip fracture. Risk of bias (ROB) was dual-assessed using the Prediction model Risk Of Bias ASsessment Tool. Study and model details were extracted and summarized.

RESULTS

From 5571 records, 80 eligible studies were identified. They predicted mortality in n = 55 studies/81 models and nonmortality outcomes (mobility, function, residence, medical, and surgical complications) in n = 30 studies/45 models. Most (n = 46; 58%) studies were published since 2020. A quarter of studies (n = 19; 24%) reported using 'machine-learning methods', while the remainder used logistic regression (n = 54; 68%) and other statistical methods (n = 11; 14%) to build models. Overall, 15 studies (19%) presented 18 low ROB models, all predicting mortality. Common concerns were sample size, missing data handling, inadequate internal validation, and calibration assessment. Many studies with nonmortality outcomes (n = 11; 37%) had clear data complexities that were not correctly modeled.

CONCLUSION

This review has comprehensively summarized and appraised multivariable prediction models for long-term outcomes after hip fracture. Only 15 studies of 55 predicting mortality were rated as low ROB, warranting further development of their models. All studies predicting nonmortality outcomes were high or unclear ROB. Careful consideration is required for both the methods used and justification for developing further nonmortality prediction models for this clinical population.

Derivation and Validation of a Scoring System for Predicting Poor Outcome After Posterior Circulation Ischemic Stroke in China

BACKGROUND AND OBJECTIVES

Guidelines for posterior circulation ischemic stroke (PCIS) treatment are lacking and outcome prediction is crucial for patients and clinicians. We aimed to develop and validate a prognostic score to predict the poor outcome for patients with PCIS.

METHODS

The score was developed from a prospective derivation cohort named the Third China National Stroke Registry (August 2015-March 2018) and validated in a spatiotemporal independent validation cohort (December 2017-March 2023) in China. Patients with PCIS with acute infarctions defined as hyperintense lesions on diffusion-weighted imaging were included in this study. The poor outcome was measured as modified Rankin scale (mRS) score 3-6 at 3 months after PCIS. Multivariable logistic regression analysis was used to identify predictors for poor outcome. The prognostic score, namely PCIS Outcome Score (PCISOS), was developed by assigning points to variables based on their relative β-coefficients in the logistic model.

RESULTS

The PCISOS was derived from 3,294 patients (median age 62 [interquartile range (IQR) 55-70] years; 2,250 [68.3%] men) and validated in 501 patients (median age 61 [IQR 53-68] years; 404 [80.6%] men). Among them, 384 (11.7%) and 64 (12.8%) had poor outcome 3 months after stroke in respective cohorts. Age, mRS before admission, NIH Stroke Scale on admission, ischemic stroke history, infarction distribution, basilar artery, and posterior cerebral artery stenosis or occlusion were identified as independent predictors for poor outcome and included in PCISOS. This easy-to-use integer scoring system identified a marked risk gradient between 4 risk groups. PCISOS performed better than previous scores, with an excellent discrimination (C statistic) of 0.80 (95% CI 0.77-0.83) in the derivation cohort and 0.81 (95% CI 0.77-0.84) in the validation cohort. Calibration test showed high agreement between the predicted and observed outcomes in both cohorts.

DISCUSSION

PCISOS can be applied for patients with PCIS with acute infarctions to predict functional outcome at 3 months post-PCIS. This simple tool helps clinicians to identify patients with PCIS with higher risk of poor outcome and provides reliable outcome expectations for patients. This information might be used for personalized rehabilitation plan and patient selection for future clinical trials to reduce disability and mortality.

Multivariable prognostic modelling to improve prediction of colorectal cancer recurrence: the PROSPeCT trial

OBJECTIVE

Improving prognostication to direct personalised therapy remains an unmet need. This study prospectively investigated promising CT, genetic, and immunohistochemical markers to improve the prediction of colorectal cancer recurrence.

MATERIAL AND METHODS

This multicentre trial (ISRCTN 95037515) recruited patients with primary colorectal cancer undergoing CT staging from 13 hospitals. Follow-up identified cancer recurrence and death. A baseline model for cancer recurrence at 3 years was developed from pre-specified clinicopathological variables (age, sex, tumour-node stage, tumour size, location, extramural venous invasion, and treatment). Then, CT perfusion (blood flow, blood volume, transit time and permeability), genetic (RAS, RAF, and DNA mismatch repair), and immunohistochemical markers of angiogenesis and hypoxia (CD105, vascular endothelial growth factor, glucose transporter protein, and hypoxia-inducible factor) were added to assess whether prediction improved over tumour-node staging alone as the main outcome measure.

RESULTS

Three hundred twenty-six of 448 participants formed the final cohort (226 male; mean 66 ± 10 years. 227 (70%) had ≥ T3 stage cancers; 151 (46%) were node-positive; 81 (25%) developed subsequent recurrence. The sensitivity and specificity of staging alone for recurrence were 0.56 [95% CI: 0.44, 0.67] and 0.58 [0.51, 0.64], respectively. The baseline clinicopathologic model improved specificity (0.74 [0.68, 0.79], with equivalent sensitivity of 0.57 [0.45, 0.68] for high vs medium/low-risk participants. The addition of prespecified CT perfusion, genetic, and immunohistochemical markers did not improve prediction over and above the clinicopathologic model (sensitivity, 0.58-0.68; specificity, 0.75-0.76).

CONCLUSION

A multivariable clinicopathological model outperformed staging in identifying patients at high risk of recurrence. Promising CT, genetic, and immunohistochemical markers investigated did not further improve prognostication in rigorous prospective evaluation.

CLINICAL RELEVANCE STATEMENT

A prognostic model based on clinicopathological variables including age, sex, tumour-node stage, size, location, and extramural venous invasion better identifies colorectal cancer patients at high risk of recurrence for neoadjuvant/adjuvant therapy than stage alone.

KEY POINTS

Identification of colorectal cancer patients at high risk of recurrence is an unmet need for treatment personalisation. This model for recurrence, incorporating many patient variables, had higher specificity than staging alone. Continued optimisation of risk stratification schema will help individualise treatment plans and follow-up schedules.

Predictive factors for postoperative outcomes after reverse shoulder arthroplasty: a systematic review

BACKGROUND

The use of reverse total shoulder arthroplasty (RTSA) has increased at a greater rate than other shoulder procedures. In general, clinical and functional outcomes after RTSA have been favorable regardless of indication. However, little evidence exists regarding patient specific factors associated with clinical improvement after RTSA. Predicting postoperative outcomes after RTSA may support patients and physicians to establish more accurate patient expectations and contribute in treatment decisions. The aim of this study was to determine predictive factors for postoperative outcomes after RTSA for patients with degenerative shoulder disorders.

METHODS

EMBASE, PubMed, Cochrane Library and PEDro were searched to identify cohort studies reporting on predictive factors for postoperative outcomes after RTSA. Authors independently screened publications on eligibility. Risk of bias for each publication was assessed using the QUIPS tool. A qualitative description of the results was given. The GRADE framework was used to establish the quality of evidence.

RESULTS

A total of 1986 references were found of which 11 relevant articles were included in the analysis. Risk of bias was assessed as low (N = 7, 63.6%) or moderate (N = 4, 36.4%). According to the evidence synthesis there was moderate-quality evidence indicating that greater height predicts better postoperative shoulder function, and greater preoperative range of motion (ROM) predicts increased postoperative ROM following.

CONCLUSION

Preoperative predictive factors that may predict postoperative outcomes are: patient height and preoperative range of motion. These factors should be considered in the preoperative decision making for a RTSA, and can potentially be used to aid in preoperative decision making.

LEVEL OF EVIDENCE

Level I; Systematic review.

GRADE concept paper 8: judging the certainty of discrimination performance estimates of prognostic models in a body of validation studies

BACKGROUND

Prognostic models incorporate multiple prognostic factors to estimate the likelihood of future events for individual patients based on their prognostic factor values. Evaluating these models crucially involves conducting studies to assess their predictive performance, like discrimination. Systematic reviews and meta-analyses of these validation studies play an essential role in selecting models for clinical practice.

METHODS

In this paper, we outline 3 thresholds to determine the target for certainty rating in the discrimination of prognostic models, as observed across a body of validation studies.

RESULTS AND CONCLUSION

We propose 3 thresholds when rating the certainty of evidence about a prognostic model's discrimination. The first threshold amounts to rating certainty in the model's ability to classify better than random chance. The other 2 approaches involve setting thresholds informed by other mechanisms for classification: clinician intuition or an alternative prognostic model developed for the same disease area and outcome. The choice of threshold will vary based on the context. Instead of relying on arbitrary discrimination cut-offs, our approach positions the observed discrimination within an informed spectrum, potentially aiding decisions about a prognostic model's practical utility.

External validation of a paediatric Smart triage model for use in resource limited facilities

Models for digital triage of sick children at emergency departments of hospitals in resource poor settings have been developed. However, prior to their adoption, external validation should be performed to ensure their generalizability. We externally validated a previously published nine-predictor paediatric triage model (Smart Triage) developed in Uganda using data from two hospitals in Kenya. Both discrimination and calibration were assessed, and recalibration was performed by optimizing the intercept for classifying patients into emergency, priority, or non-urgent categories based on low-risk and high-risk thresholds. A total of 2539 patients were eligible at Hospital 1 and 2464 at Hospital 2, and 5003 for both hospitals combined; admission rates were 8.9%, 4.5%, and 6.8%, respectively. The model showed good discrimination, with area under the receiver-operator curve (AUC) of 0.826, 0.784 and 0.821, respectively. The pre-calibrated model at a low-risk threshold of 8% achieved a sensitivity of 93% (95% confidence interval, (CI):89%-96%), 81% (CI:74%-88%), and 89% (CI:85%-92%), respectively, and at a high-risk threshold of 40%, the model achieved a specificity of 86% (CI:84%-87%), 96% (CI:95%-97%), and 91% (CI:90%-92%), respectively. Recalibration improved the graphical fit, but new risk thresholds were required to optimize sensitivity and specificity.The Smart Triage model showed good discrimination on external validation but required recalibration to improve the graphical fit of the calibration plot. There was no change in the order of prioritization of patients following recalibration in the respective triage categories. Recalibration required new site-specific risk thresholds that may not be needed if prioritization based on rank is all that is required. The Smart Triage model shows promise for wider application for use in triage for sick children in different settings.

A brief step-by-step guide on conducting a systematic review and meta-analysis of prognostic model studies

Prognostic models provide an avenue to predict the risk of individual patients and support shared-decision making. Many prognostic models are published annually, and systematic reviews provide an avenue to collate the existing evidence behind prognostic models to determine whether a model demonstrates adequate predictive performance and is ready for real-world use. This article provides a brief step-by-step guide on how to conduct a systematic review and meta-analysis of prognostic model studies and how these reviews differ from systematic reviews of therapy and diagnosis.

R3-Walk and R6-Walk, Simple Clinical Equations to Accurately Predict Independent Walking at 3 and 6 Months After Stroke: A Prospective, Cohort Study

OBJECTIVE

To investigate if independent walking at 3 and 6 months poststroke can be accurately predicted within the first 72 hours, based on simple clinical bedside tests.

DESIGN

Prospective observational cohort study with 3-time measurements: immediately after stroke, and 3 and 6 months poststroke.

SETTING

Public hospital.

PARTICIPANTS

Adults with first-ever stroke evaluated at 3 (N=263) and 6 (N=212) months poststroke.

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

The outcome of interest was independent walking at 3 and 6 months after stroke. Predictors were age, walking ability, lower limb strength, motor recovery, spatial neglect, continence, and independence in activities of daily living.

RESULTS

The equation for predicting walking 3 months poststroke was 3.040 + (0.283 × FAC baseline) + (0.021 × Modified Barthel Index), and for predicting walking 6 months poststroke was 3.644 + (-0.014 × age) + (0.014 × Modified Barthel Index). For walking ability 3 months after stroke, sensitivity was classified as high (91%; 95% CI: 81-96), specificity was moderate (57%; 95% CI: 45-69), positive predictive value was high (76%; 95% CI: 64-86), and negative predictive value was high (80%; 95% CI: 60-93). For walking ability 6 months after stroke, sensitivity was classified as moderate (54%; 95% CI: 47-61), specificity was high (81%; 95% CI: 61-92), positive predictive value was high (87%; 95% CI: 70-96), and negative predictive value was low (42%; 95% CI: 50-73).

CONCLUSIONS

This study provided 2 simple equations that predict walking ability 3 and 6 months after stroke. This represents an important step to accurately identify individuals, who are at high risk of walking dependence early after stroke.

Mental health sequelae in survivors of cardiogenic shock complicating myocardial infarction. A population-based cohort study

PURPOSE

Cardiogenic shock secondary to acute myocardial infarction (AMI-CS) is associated with substantial short- and long-term morbidity and mortality. However, there are limited data on mental health sequelae that survivors experience following discharge.

METHODS

We conducted a retrospective, population-based cohort study in Ontario, Canada of critically ill adult (≥ 18 years) survivors of AMI-CS, admitted to hospital between April 1, 2009 and March 31, 2019. We compared these patients to AMI survivors without shock. We captured outcome data using linked health administrative databases. The primary outcome was a new mental health diagnosis (a composite of mood, anxiety, or related disorders; schizophrenia/psychotic disorders; and other mental health disorders) following hospital discharge. We secondarily evaluated incidence of deliberate self-harm and death by suicide. We compared patients using overlap propensity score-weighted, cause-specific proportional hazard models.

RESULTS

We included 7812 consecutive survivors of AMI-CS, from 135 centers. Mean age was 68.4 (standard deviation (SD) 12.2) years, and 70.3% were male. Median follow-up time was 767 days (interquartile range (IQR) 225-1682). Incidence of new mental health diagnosis among AMI-CS survivors was 109.6 per 1,000 person-years (95% confidence interval (CI) 105.4-113.9), compared with 103.8 per 1000 person-years (95% CI 102.5-105.2) among AMI survivors without shock. After propensity score adjustment, there was no difference in the risk of new mental health diagnoses following discharge [hazard ratio (HR) 0.99 (95% CI 0.94-1.03)]. Factors associated with new mental health diagnoses following AMI-CS included female sex, pre-existing mental health diagnoses, and discharge to a long-term hospital or rehabilitation institute.

CONCLUSION

Survivors of AMI-CS experience substantial mental health morbidity following discharge. Risk of new mental health diagnoses was comparable between survivors of AMI with and without shock. Future research on interventions to mitigate psychiatric sequelae after AMI-CS is warranted.

SPIN-PM: a consensus framework to evaluate the presence of spin in studies on prediction models

OBJECTIVES

To develop a framework to identify and evaluate spin practices and its facilitators in studies on clinical prediction model regardless of the modeling technique.

STUDY DESIGN AND SETTING

We followed a three-phase consensus process: (1) premeeting literature review to generate items to be included; (2) a series of structured meetings to provide comments discussed and exchanged viewpoints on items to be included with a panel of experienced researchers; and (3) postmeeting review on final list of items and examples to be included. Through this iterative consensus process, a framework was derived after all panel's researchers agreed.

RESULTS

This consensus process involved a panel of eight researchers and resulted in SPIN-Prediction Models which consists of two categories of spin (misleading interpretation and misleading transportability), and within these categories, two forms of spin (spin practices and facilitators of spin). We provide criteria and examples.

CONCLUSION

We proposed this guidance aiming to facilitate not only the accurate reporting but also an accurate interpretation and extrapolation of clinical prediction models which will likely improve the reporting quality of subsequent research, as well as reduce research waste.

Clinical Risk Assessment and Prediction in Congenital Heart Disease Across the Lifespan: JACC Scientific Statement

Congenital heart disease (CHD) comprises a range of structural anomalies, each with a unique natural history, evolving treatment strategies, and distinct long-term consequences. Current prediction models are challenged by generalizability, limited validation, and questionable application to extended follow-up periods. In this JACC Scientific Statement, we tackle the difficulty of risk measurement across the lifespan. We appraise current and future risk measurement frameworks and describe domains of risk specific to CHD. Risk of adverse outcomes varies with age, sex, genetics, era, socioeconomic status, behavior, and comorbidities as they evolve through the lifespan and across care settings. Emerging technologies and approaches promise to improve risk assessment, but there is also need for large, longitudinal, representative, prospective CHD cohorts with multidimensional data and consensus-driven methodologies to provide insight into time-varying risk. Communication of risk, particularly with patients and their families, poses a separate and equally important challenge, and best practices are reviewed.