Dataset Information

Daily Assessment of Acute Dynamic Risk in Paroled Offenders: Prediction, Predictive Accuracy and Intervention Effect.

ABSTRACT: Few studies have assessed acute dynamic risk repeatedly among paroled offenders to investigate the relationship between changes in acute dynamic risk and recidivism in crime. The present study investigates whether one-month changes in ten stress-related acute dynamic risk factors, collected through automated telephony while the participants were still in prison and over 30 consecutive days following parole, predict one-year criminal recidivism, including its predictive accuracy. The study also investigates whether a brief feedback intervention in conjunction with the daily assessments reduces recidivism compared to an assessment-only control group. Changes in five risk factors were found to be associated with increased risk of criminal recidivism after controlling for the results in prison, the initial value after parole, and the intervention. The predictive accuracy is marginally accurate: Summary score (AUC) = .666; Level of stress (AHSS) = .644; Psychiatric symptoms (SCL-8D) = .641; Anxiety symptoms = .673; Severity of most stressful daily event = .690. No differences in one-year recidivism rates were established between the intervention group and the control group. The study shows that daily assessments can usefully be made of dynamic risk factors in paroled offenders.

SUBMITTER: Vasiljevic Z

PROVIDER: S-EPMC6820061 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Daily Assessment of Acute Dynamic Risk in Paroled Offenders: Prediction, Predictive Accuracy and Intervention Effect.

Vasiljevic Zoran Z Berglund Mats M Öjehagen Agneta A Höglund Peter P Andersson Claes C

Psychiatry, psychology, and law : an interdisciplinary journal of the Australian and New Zealand Association of Psychiatry, Psychology and Law 20170430 5

Few studies have assessed acute dynamic risk repeatedly among paroled offenders to investigate the relationship between changes in acute dynamic risk and recidivism in crime. The present study investigates whether one-month changes in ten stress-related acute dynamic risk factors, collected through automated telephony while the participants were still in prison and over 30 consecutive days following parole, predict one-year criminal recidivism, including its predictive accuracy. The study also i ...[more]

PMID: 31983984

Similar Datasets

Project description:ImportanceEffective treatment of acute kidney injury (AKI) is predicated on timely diagnosis; however, the lag in the increase in serum creatinine levels after kidney injury may delay therapy initiation.ObjectiveTo determine the derivation and validation of predictive models for AKI after cardiac surgery.Design, setting, and participantsMultivariable prediction models were derived based on a retrospective observational cohort of adult patients undergoing cardiac surgery between January 2000 and December 2019 from a US academic medical center (n = 58 526) and subsequently validated on an external cohort from 3 US community hospitals (n = 4734). The date of final follow-up was January 15, 2020.ExposuresPerioperative change in serum creatinine and postoperative blood urea nitrogen, serum sodium, potassium, bicarbonate, and albumin from the first metabolic panel after cardiac surgery.Main outcomes and measuresArea under the receiver-operating characteristic curve (AUC) and calibration measures for moderate to severe AKI, per Kidney Disease: Improving Global Outcomes (KDIGO), and AKI requiring dialysis prediction models within 72 hours and 14 days following surgery.ResultsIn a derivation cohort of 58 526 patients (median [IQR] age, 66 [56-74] years; 39 173 [67%] men; 51 503 [91%] White participants), the rates of moderate to severe AKI and AKIrequiring dialysis were 2674 (4.6%) and 868 (1.48%) within 72 hours and 3156 (5.4%) and 1018 (1.74%) within 14 days after surgery. The median (IQR) interval to first metabolic panel from conclusion of the surgical procedure was 10 (7-12) hours. In the derivation cohort, the metabolic panel-based models had excellent predictive discrimination for moderate to severe AKI within 72 hours (AUC, 0.876 [95% CI, 0.869-0.883]) and 14 days (AUC, 0.854 [95% CI, 0.850-0.861]) after the surgical procedure and for AKI requiring dialysis within 72 hours (AUC, 0.916 [95% CI, 0.907-0.926]) and 14 days (AUC, 0.900 [95% CI, 0.889-0.909]) after the surgical procedure. In the validation cohort of 4734 patients (median [IQR] age, 67 (60-74) years; 3361 [71%] men; 3977 [87%] White participants), the models for moderate to severe AKI after the surgical procedure showed AUCs of 0.860 (95% CI, 0.838-0.882) within 72 hours and 0.842 (95% CI, 0.820-0.865) within 14 days and the models for AKI requiring dialysis and 14 days had an AUC of 0.879 (95% CI, 0.840-0.918) within 72 hours and 0.873 (95% CI, 0.836-0.910) within 14 days after the surgical procedure. Calibration assessed by Spiegelhalter z test showed P >.05 indicating adequate calibration for both validation and derivation models.Conclusions and relevanceAmong patients undergoing cardiac surgery, a prediction model based on perioperative basic metabolic panel laboratory values demonstrated good predictive accuracy for moderate to severe acute kidney injury within 72 hours and 14 days after the surgical procedure. Further research is needed to determine whether use of the risk prediction tool improves clinical outcomes.

Project description:BackgroundClinical prediction models often fail to generalize in the context of clustered data, because most models fail to account for heterogeneity in outcome values and covariate effects across clusters. Furthermore, standard approaches for modeling clustered data, including generalized linear mixed-effects models, would not be expected to provide accurate predictions in novel clusters, because such predictions are typically based on the hypothetical mean cluster. We hypothesized that dynamic mixed-effects models, which incorporate data from previous predictions to refine the model for future predictions, would allow for cluster-specific predictions in novel clusters as the model is updated over time, thus improving overall model generalizability.ResultsWe quantified the potential gains in prediction accuracy from using a dynamic modeling strategy in a simulation study. Furthermore, because clinical prediction models in the context of clustered data often involve outcomes that are dependent on patient volume, we examined whether using dynamic mixed-effects models would be robust to misspecification of the volume-outcome relationship. Our results indicated that dynamic mixed-effects models led to substantial improvements in prediction accuracy in clustered populations over a broad range of conditions, and were uniformly superior to static models. In addition, dynamic mixed-effects models were particularly robust to misspecification of the volume-outcome relationship and to variation in the frequency of model updating. The extent of the improvement in prediction accuracy that was observed with dynamic mixed-effects models depended on the relative impact of fixed and random effects on the outcome as well as the degree of misspecification of model fixed effects.ConclusionsDynamic mixed-effects models led to substantial improvements in prediction model accuracy across a broad range of simulated conditions. Therefore, dynamic mixed-effects models could be a useful alternative to standard static models for improving the generalizability of clinical prediction models in the setting of clustered data, and, thus, well worth the logistical challenges that may accompany their implementation in practice.

Project description:Background: The management of the moderate and severe forms of acute pancreatitis (AP) with necrosis and multiorgan failure remains a challenge. To predict the severity and mortality of AP multiple clinical, laboratory-, and imaging-based scoring systems are available. Aim: To investigate, if the computed tomography severity index (CTSI) can predict the outcomes of AP better than other scoring systems. Methods: A systematic search was performed in three databases: Pubmed, Embase, and the Cochrane Library. Eligible records provided data from consecutive AP cases and used CTSI or modified CTSI (mCTSI) alone or in combination with other prognostic scores [Ranson, bedside index of severity in acute pancreatitis (BISAP), Acute Physiology, and Chronic Health Examination II (APACHE II), C-reactive protein (CRP)] for the evaluation of severity or mortality of AP. Area under the curves (AUCs) with 95% confidence intervals (CIs) were calculated and aggregated with STATA 14 software using the metandi module. Results: Altogether, 30 studies were included in our meta-analysis, which contained the data of 5,988 AP cases. The pooled AUC for the prediction of mortality was 0.79 (CI 0.73-0.86) for CTSI; 0.87 (CI 0.83-0.90) for BISAP; 0.80 (CI 0.72-0.89) for mCTSI; 0.73 (CI 0.66-0.81) for CRP level; 0.87 (CI 0.81-0.92) for the Ranson score; and 0.91 (CI 0.88-0.93) for the APACHE II score. The APACHE II scoring system had significantly higher predictive value for mortality than CTSI and CRP (p = 0.001 and p < 0.001, respectively), while the predictive value of CTSI was not statistically different from that of BISAP, mCTSI, CRP, or Ranson criteria. The AUC for the prediction of severity of AP were 0.80 (CI 0.76-0.85) for CTSI; 0.79, (CI 0.72-0.86) for BISAP; 0.83 (CI 0.75-0.91) for mCTSI; 0.73 (CI 0.64-0.83) for CRP level; 0.81 (CI 0.75-0.87) for Ranson score and 0.80 (CI 0.77-0.83) for APACHE II score. Regarding severity, all tools performed equally. Conclusion: Though APACHE II is the most accurate predictor of mortality, CTSI is a good predictor of both mortality and AP severity. When the CT scan has been performed, CTSI is an easily calculable and informative tool, which should be used more often in routine clinical practice.

Dataset Information

Daily Assessment of Acute Dynamic Risk in Paroled Offenders: Prediction, Predictive Accuracy and Intervention Effect.

Publications

Daily Assessment of Acute Dynamic Risk in Paroled Offenders: Prediction, Predictive Accuracy and Intervention Effect.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets