Identifying infants at risk of sudden unexpected death with an automated predictive risk model

BACKGROUND/OBJECTIVE

Sudden unexpected infant death (SUID) is a common cause of infant death. We evaluated whether a predictive risk model (PRM) - Hello Baby - which was developed to stratify children by risk of entry into foster care could also identify infants at highest risk of SUID and non-fatal unsafe sleep events.

PARTICIPANTS AND SETTING

Cases: Infants with SUID or an unsafe sleep event over 5½ years in a single county.

CONTROLS

All births in the same county.

METHODS

Retrospective case-control study. Demographic and clinical data were collected and a Hello Baby PRM score was assigned. Descriptive statistics and the predictive value of a PRM score of 20 were calculated.

RESULTS

Infants with SUID (n = 62) or an unsafe sleep event (n = 37) (cases) were compared with 23,366 births (controls). Cases and controls were similar for all demographic and clinical data except that infants with unsafe sleep events were older. Median PRM score for cases was higher than controls (17.5 vs. 10, p < 0.001); 50 % of cases had a PRM score 17-20 vs. 16 % of controls (p < 0.001).

CONCLUSIONS

The Hello Baby PRM can identify newborns at high risk of SUID and non-fatal unsafe sleep events. The ability to identify high-risk newborns prior to a negative outcome allows for individualized evaluation of high-risk families for modifiable risk factors which are potentially amenable to intervention. This approach is limited by the fact that not all counties can calculate a PRM or similar score automatically.

Evaluation of two European risk models for predicting medication harm in an Australian patient cohort

PURPOSE

To externally evaluate the performance of two European risk prediction models, for identifying patients at high-risk of medication harm, in an Australian hospital setting.

METHODS

This was a secondary analysis of a pre-existing cohort study described in a recently published study by Falconer et al. (Br J Clin Pharmacol 87(3):1512-1524, 2021) describing the development of a predictive risk model for inpatient medication harm. We retrospectively extracted relevant variables using the electronic health records of general medical and geriatric patients admitted to a quaternary hospital, in Brisbane, over 6 months from July to December 2017. This dataset was used to externally evaluate the two European models, The Brighton Adverse Drug Reaction Risk (BADRI) model by Tangiisuran et al. and a risk model developed by Trivalle et al. The variables were entered into both models and the patients' risk of medication harm was calculated, and compared with actual patient outcomes. Predictive performance was evaluated by measuring area under the receiver operative characteristic (AuROC) curves.

RESULTS

The Australian patient cohort included 1982 patients (median age 74 years), of which 136 (7%) patients experienced ≥ 1 medication harm event(s). External evaluation of the two European models identified that both the BADRI and the Trivalle models had reduced predictive performance in an Australian patient cohort, compared with their original studies (AuROC of 0.63 [95% CI: 0.58-0.68] and 0.60 [95% CI: 0.55-0.65], respectively).

CONCLUSION

Neither model demonstrated sufficient discrimination to warrant further evaluation in our local setting. This is likely a result of variations between the development and the validation cohorts, and the change in healthcare systems over time, and highlights the need for an up-to-date and context-specific risk prediction model.

Surgical Risk Following Anatomic Lung Resection in Thoracic Surgery: A Prediction Model Derived from a Spanish Multicenter Database

INTRODUCTION

The aim of this study was to develop a surgical risk prediction model in patients undergoing anatomic lung resections from the registry of the Spanish Video-Assisted Thoracic Surgery Group (GEVATS).

METHODS

Data were collected from 3,533 patients undergoing anatomic lung resection for any diagnosis between December 20, 2016 and March 20, 2018. We defined a combined outcome variable: death or Clavien Dindo grade IV complication at 90 days after surgery. Univariate and multivariate analyses were performed by logistic regression. Internal validation of the model was performed using resampling techniques.

RESULTS

The incidence of the outcome variable was 4.29% (95% CI 3.6-4.9). The variables remaining in the final logistic model were: age, sex, previous lung cancer resection, dyspnea (mMRC), right pneumonectomy, and ppo DLCO. The performance parameters of the model adjusted by resampling were: C-statistic 0.712 (95% CI 0.648-0.750), Brier score 0.042 and bootstrap shrinkage 0.854.

CONCLUSIONS

The risk prediction model obtained from the GEVATS database is a simple, valid, and reliable model that is a useful tool for establishing the risk of a patient undergoing anatomic lung resection.

A predictive risk model for nonfatal opioid overdose in a statewide population of buprenorphine patients

BACKGROUND

Predicting which individuals who are prescribed buprenorphine for opioid use disorder are most likely to experience an overdose can help target interventions to prevent relapse and subsequent consequences.

METHODS

We used Maryland prescription drug monitoring data from 2015 to identify risk factors for nonfatal opioid overdoses that were identified in hospital discharge records in 2016. We developed a predictive risk model for prospective nonfatal opioid overdoses among buprenorphine patients (N = 25,487). We estimated a series of models that included demographics plus opioid, buprenorphine and benzodiazepine prescription variables. We applied logistic regression to generate performance measures.

RESULTS

About 3.24% of the study cohort had ≥1 nonfatal opioid overdoses. In the model with all predictors, odds of nonfatal overdoses among buprenorphine patients were higher among males (OR = 1.39, 95% CI:1.21-1.62) and those with more buprenorphine pharmacies (OR = 1.19, 95% CI:1.11-1.28), 1+ buprenorphine prescription paid by Medicaid (OR = 1.21, 95% CI:1.02-1.48), Medicare (OR = 1.93, 95% CI:1.63-2.43), or a commercial plan (OR = 1.98, 95% CI:1.30-2.89), 1+ opioid prescription paid by Medicare (OR = 1.30, 95% CI:1.03-1.68), and more benzodiazepine prescriptions (OR = 1.04, 95% CI:1.02-1.05). The odds were lower among those with longer days of buprenorphine (OR = 0.64, 95% CI:0.60-0.69) or opioid (OR = 0.79, 95% CI:0.65-0.95) supply. The model had moderate predictive ability (c-statistic = 0.69).

CONCLUSIONS

Several modifiable risk factors, such as length of buprenorphine treatment, may be targets for interventions to improve clinical care and reduce harms. This model could be practically implemented with common prescription-related information and allow payers and clinical systems to better target overdose risk reduction interventions, such as naloxone distribution.

Clinical factors related to morbidity and mortality in high-risk heart failure patients: the GUIDE-IT predictive model and risk score

BACKGROUND

Most heart failure (HF) risk scores have been derived from cohorts of stable HF patients and may not incorporate up to date treatment regimens or deep phenotype characterization that change baseline risk over the short- and long-term follow-up period. We undertook the current analysis of participants in the GUIDE-IT (Guiding Evidence-Based Therapy Using Biomarker Intensified Treatment) trial to address these limitations.

METHODS AND RESULTS

The GUIDE-IT study randomized 894 high-risk patients with HF and reduced ejection fraction (≤ 40%) to biomarker-guided treatment strategy vs. usual care. We performed risk modelling using Cox proportional hazards models and analysed the relationship between 35 baseline clinical factors and the primary composite endpoint of cardiovascular (CV) death or HF hospitalization, the secondary endpoint of all-cause mortality, and the exploratory endpoint of 90-day HF hospitalization or death. Prognostic relationships for continuous variables were examined and key predictors were identified using a backward variable selection process. Predictive models and risk scores were developed. Over a median follow-up of 15 months, the cumulative number of HF hospitalizations and CV deaths was 328 out of 894 patients (Kaplan-Meier event rate 34.5% at 12 months). Frequency of all-cause deaths was 143 out of 894 patients (Kaplan-Meier event rate 12.2% at 12 months). Outcomes for the primary and secondary endpoints between strategy arms of the study were similar. The most important predictor that was present in all three models was the baseline natriuretic peptide level. Hispanic ethnicity, low sodium and high heart rate were present in two of the three models. Other important predictors included the presence or absence of a device, New York Heart Association class, HF duration, black race, co-morbidities (sleep apnoea, elevated creatinine, ischaemic heart disease), low blood pressure, and a high congestion score.

CONCLUSION

Risk models using readily available clinical information are able to accurately predict short- and long-term CV events and may be useful in optimizing care and enriching patients for clinical trials.

CLINICAL TRIAL REGISTRATION

ClinicalTrials.gov ID number NCT01685840.

A predictive risk model for medical intractability in epilepsy

OBJECTIVE

This study aimed to investigate early predictors (6 months after diagnosis) of medical intractability in epilepsy.

METHODS

All children <12 years of age having two or more unprovoked seizures 24 h apart at Xinhua Hospital between 1992 and 2006 were included. Medical intractability was defined as failure, due to lack of seizure control, of more than 2 antiepileptic drugs at maximum tolerated doses, with an average of more than 1 seizure per month for 24 months and no more than 3 consecutive months of seizure freedom during this interval. Univariate and multivariate logistic regression models were performed to determine the risk factors for developing medical intractability. Receiver operating characteristic curve was applied to fit the best compounded predictive model.

RESULTS

A total of 649 patients were identified, out of which 119 (18%) met the study definition of intractable epilepsy at 2 years after diagnosis, and the rate of intractable epilepsy in patients with idiopathic syndromes was 12%. Multivariate logistic regression analysis revealed that neurodevelopmental delay, symptomatic etiology, partial seizures, and more than 10 seizures before diagnosis were significant and independent risk factors for intractable epilepsy. The best model to predict medical intractability in epilepsy comprised neurological physical abnormality, age at onset of epilepsy under 1 year, more than 10 seizures before diagnosis, and partial epilepsy, and the area under receiver operating characteristic curve was 0.7797. This model also fitted best in patients with idiopathic syndromes.

CONCLUSION

A predictive model of medically intractable epilepsy composed of only four characteristics is established. This model is comparatively accurate and simple to apply clinically.